Time series data analysis, employing dynamic time warping (DTW) algorithms, has a wide range of applications in fields such as medicine and economics. Given the widespread distribution of data across different domains, integrating and analyzing these datasets through outsourced cloud computing can enhance analytics, though privacy concerns arise. Privacy preserving data analysis, underpinned by secure multi-party computing, emerges as a crucial approach to address this challenge. However, existing efforts face high communication costs and increased interactions, resulting in significant efficiency constraints in practical applications. In this paper, we propose a function secret sharing (FSS)-based framework for secure collaborative analysis of time series data using the DTW algorithm. Utilizing the distributed comparison function, we develop efficient building blocks with minimal online interaction and communication, enhancing the practicability of security protocols. To address the challenges of FSS key generation due to uncertain computational topology when cascading multiple distances, we adopt a modular design and decompose the analysis process into several critical modules. Furthermore, our framework efficiently supports various constraint methods for DTW. We implement and evaluate our framework using publicly available datasets. The results demonstrate a significant reduction in communication costs and the number of interactions during the online phase.

Radar sensing has emerged as a promising solution for the contactless monitoring of Heart Rate Variability (HRV), a crucial indicator of the cardiovascular and autonomic nervous systems. However, due to signal noise and interference that easily obscure heartbeat details, along with variations in heartbeat across different physiological conditions, existing methods remain restricted to laboratory settings with healthy subjects and fail in real-world scenarios involving more complex physiological conditions. In this study, we propose a physiological prior-inspired deep learning framework for robust radar-based HRV monitoring. Specifically, we leverage the prior that internal heartbeats drive movements across the entire torso surface and design a hybrid deep neural network to model the spatio-temporal relationship between full-body radio reflections and heartbeats, effectively mitigating interference. Then, we incorporate the cardiac motion's self-similarity prior to establish a signal augmentation strategy, effectively remodeling the HRV distribution and enhancing performance across diverse physiological conditions. We build and validate our method on a large-scale dataset comprising 7,150 outpatients with complex physiological conditions in real-world scenarios. The experimental results demonstrate that our method achieves a mean IBI error of 19.21 ms, an RMSSD error of 16.23 ms, an SDSD error of 16.70 ms, and a pNN50 error of 7.28%. We further validate the performance by classifying five common cardiac conditions based on HRV results, demonstrating performance comparable to ECG-based methods. These results highlight the great potential of our approach for accurate, contactless HRV monitoring in real-world applications.

Arrhythmias are prevalent cardiac disorders affecting millions worldwide. By analyzing cardiac motion modulated in mmWave reflections, mmWave sensing is emerging as a promising contactless revolution in arrhythmia detection compared to conventional contact-based methods. However, the fundamental bottleneck of existing mmWave sensing methods is their restriction to controlled laboratory settings with small-scale cohorts, limiting generalization to real-world populations. This limitation arises because mmWave signals undergo complex signal transformations during propagation, resulting in an explosion of signal diversity across large populations in real-world scenarios. Such diversity significantly complicates the direct recognition of arrhythmia. In this paper, we theoretically analyze the mechanism and impact of mmWave cardiac signal diversity. Leveraging the inherent transformation properties of mmWave signals, we propose a Diversity-Invariant Contrastive mmWave Sensing framework, which learns invariant features robust to complex signal transformations encountered in real-world scenarios. We evaluate our method in a practical, clinically-oriented scenario involving a large-scale population of 7,338 subjects, achieving an average F1-score of 0.8241 across four common arrhythmias. These results demonstrate that our method effectively bridges the diversity gap, representing a significant step toward practical clinical deployment of contactless arrhythmia detection via mmWave sensing.

Contactless stress monitoring, with its non-intrusive nature, is invaluable for maintaining mental and physical health. Recent studies have demonstrated encouraging results in contactless stress monitoring during daytime using radio frequency (RF) signals. However, the weak correlation between stress levels and behaviors during the night poses a significant challenge in stress monitoring, which remains unsolved. In this paper, we propose mmWave Nighttime Stress monitoring (mmNS), a learning-based end-to-end framework for contactless nighttime stress monitoring. Specifically, this framework incorporates radar signal processing and a self-supervised physiological feature separation strategy, combined with a signal complexity-oriented network design, to effectively extract and encode periodic physiological features for accurate stress level classification. To evaluate the stress monitoring performance of mmNS, we collect a RF-based nighttime stress monitoring dataset, which contains stress data from 10 volunteers. The experimental results demonstrate that our method achieves state-of-the-art stress monitoring performance, about 76% accuracy and 72% F1-score in classifying low, medium and high stress. To our knowledge, this is the first attempt dealing with contactless nighttime stress monitoring.

Entanglement distribution between distant quantum nodes plays an important role in quantum networks. However, due to the unique properties of quantum mechanics and hardware limitations, entanglement resources in quantum networks are scarce. Quantum links that fail to meet request demands become bottleneck links, significantly hindering remote entanglement distribution in multi-request scenarios. In this paper, we propose an entanglement routing scheme called SwappingBoost that can effectively reduce resource consumption along entanglement distribution paths, alleviating the negative impact of bottleneck links. SwappingBoost first employs a decreasing resource reservation method to compensate for resource losses caused by failed entanglement swapping, freeing up pre-reserved resources on downstream links to accommodate other paths and requests. Besides, SwappingBoost introduces a path-priority-based rounding algorithm that achieves integer-level resource allocation while ensuring balanced resource allocation. Extensive simulation results demonstrate that SwappingBoost can effectively reduce the load of bottleneck links, enhancing network throughput while maintaining fairness among multiple requests.

Radio sensing has emerged as a promising contactless revolution for cardiac monitoring. However, considering the complexity of radio propagation, extracting stable and clinically meaningful features remains challenging, posing a barrier to scaling this technology for practical and clinical reliable deployment. In this demo, we present RadioCardiogram, a system that leverages AI-powered knowledge transfer from well-established ECG diagnostic paradigms to accurately interpret complex radio signals. It enables all-in-one cardiac function monitoring including heart rate variability analysis, arrhythmia detection, and ECG-aligned waveform reconstruction. The system is implemented in a mobile phone-sized prototype and validated in a large-scale, clinically oriented cohort involving 6,258 outpatient visitors. Results demonstrate performance approaching the gold standard in both HRV monitoring and arrhythmia detection, highlighting a pathway toward effortless continuous, and reliable cardiac health coverage in real-world usage. The demo video is available at the following link.

In recent years, multi-hop Quantum Key Distribution (QKD) network has been proven as a promising solution through rigorous practices to provide end-to-end key exchange service for arbitrary communication parties. However, existing decentralized solutions still face critical challenges including consistency and fairness that stem from storable nature of quantum key material. Thus, in this paper, we first devise a Key Management and Service (KM&S) framework for decentralized multi-hop QKD networks, which provides functional decoupling and pipeline processing to guarantee flexibility and compatibility for practical implementation. After that, to address consistency and fairness challenges during end-to-end key exchange service, we focus on two aspects including local key management and end-to-end congestion control, and respectively propose an elastic key supply rate control scheme named AUTO and a Capacity Probing-driven Backpressure Flow Control (CP-BFC) scheme. Furthermore, we construct an experiment platform equipped with realistic QKD devices based on China metropolitan QKD network topology to implement the proposed framework and schemes, and conduct extensive experiments compared to representative schemes in existing studies. The experimental results show that AUTO&CP-BFC significantly outperforms representative schemes in terms of consistency and fairness.

Quantum communication networks enable advanced quantum applications through remote entanglement distribution among source-destination pairs. Despite efforts to optimize entanglement distribution, fairness in multi-request scenarios has been neglected, potentially causing issues like “request starvation”. To address such issue, this paper concentrates on the unique properties of entangled systems and introduces a process-oriented fairness metric, i.e., expected throughput, departing from conventional approaches used in classical networks. Furthermore, we propose an entanglement distribution scheme named Fair-EAS, which prioritizes entanglement allocation and selection for batching multiple requests to maximize overall throughput while maintaining max-min fairness. To facilitate a convenient solution, we transform the nonlinearity of the problem into an equivalent linear programming formulation and decouple the solution into offline and online phases. In the offline phase, we design a multi-round water-filling-like optimization algorithm to determine the optimal path set for predicting entanglement allocation. In the online phase, we introduce an adaptive compensation algorithm and an entanglement “fragment” exhaustion algorithm to dynamically adjust the path set based on successfully generated entangled pairs. Comprehensive simulations show that Fair-EAS outperforms the existing schemes in terms of fairness by significantly enhancing the minimum throughput and throughput deviation among multiple requests while maintaining an overall throughput close to the optimal level.

Electronic voting plays a crucial role in facilitating democratic and convenient decision-making in people’s lives. However, implementing an electronic voting system poses challenges, such as meeting the stringent security requirements for anonymity, fairness, and verifiability. Another concern is the performance degradation when dealing with a large number of voters. In this paper, we propose$S^{3}$Voting, a blockchain sharding-based e-voting scheme that addresses these challenges. By combining robust security and scalability,$S^{3}$Voting provides reliable technical support for conducting large-scale elections. Utilizing advanced technologies such asHomomorphic Time-Lock Puzzle (HTLP)andone-time ring signature, the system safeguards voters’ privacy and ballot confidentiality. The approach involves dividing voters and miners into smaller shards, and implementing shard managing mechanisms to ensure security and enhance system efficiency. Through thorough security analysis, we demonstrate that$S^{3}$Voting not only meets the fundamental security requirements of e-voting but also offers verifiability and strong robustness-essential elements for successful large-scale elections. Moreover, experimental results indicate that$S^{3}$Voting significantly reduces the computational burden on individual miners and minimizes system processing time compared to existing blockchain-based e-voting solutions.

The surge in blockchain-based cryptocurrencies has created a pressing need for Cross-Chain Transaction (CCTx) solutions. Existing solutions either lack sufficient security, like centralized exchanges, or suffer from poor efficiency and scalability, such as atomic swaps. Inspired by the success of the Lightning Network in accelerating Bitcoin transactions, we propose CrossChannel that establishes cross-and-off-chain micropayment channels to achieve efficient and scalable CCTx. Specifically, we analyze the challenges of extending one-chain channels to cross-chain scenarios caused by the separation of blockchains. To overcome these challenges, we employ the chain relay mechanism to synchronize channel-related information across blockchains and construct the channel management protocol on this basis, ensuring the same security level as one-chain channels in cross-chain settings. We prototype CrossChannel between two Ethereum testnets, comparing its transaction efficiency and costs with a typical HTLC-swap scheme. Results demonstrate the significant advancements in efficiency and scalability offered by CrossChannel. Even with channels closing after just 20 transactions, CrossChannel exhibits a fivefold capacity increase for handling CCTxs compared to HTLC swaps.

The lack of privacy-preserving capabilities hinders the further development of blockchains and smart contracts. While numerous privacy solutions have been proposed, limitations persist. First, most existing solutions focus on specific privacy protections such as anonymous payments, private data, or multi-party computation tasks. However, these solutions lack a general privacy ability, allowing users to deploy applications with diverse privacy requirements. Second, existing solutions have limited customizability, which means users cannot easily customize and adapt the privacy policies according to their specific demands or preferences. In this article, we present EtherCloak, which adopts trusted execution environments (TEEs) to achieve a general and customizable privacy policy on account model blockchains, enabling users to conceal any on-chain information. To address the security issues caused by the unreliability of the host the TEE runs on, we design the enclave state check and crash recovery mechanisms and employ them in the block generation process. In addition, we propose an access control mechanism for privacy policy management and data query. We prove that EtherCloak offers general and customizable privacy protection with a minimal increase in transaction size (less than triple) and communication overhead (approximately 10%) compared to Ethereum.

The widespread adoption of cloud storage has raised considerable data privacy concerns for outsourced databases. In recent years, Structured Encryption (STE) has emerged as a promising solution to build encrypted databases that efficiently handle queries while preserving privacy through underlying structures called Encrypted Multi-Maps (EMMs). However, current STE-based schemes primarily focus on static settings, and their direct extensions to dynamic settings introduce significant challenges in client storage overhead and update efficiency with join condition. In this paper, we present an efficient dynamic encrypted database scheme supporting large-scale data. To address the challenges in dynamic settings, we first propose a novel dynamic EMM design with constant client storage that utilizes a global counter to reduce client storage overhead. We then introduce an algorithm for dynamically handling join queries based on tags generated from values of the join attribute, significantly reducing update overhead. We implement our scheme and conduct comparative analyses with existing dynamic STE schemes. The experimental results demonstrate that our scheme offers significant advantages in terms of client storage overhead and update performance.

Searchable symmetric encryption (SSE) is a technique that enables secure outsourcing of data to an untrusted cloud server without sacrificing search functionality. Recently, multi-user SSE schemes for data sharing, which support access control from various users, have gained attention. However, the access control mechanisms in existing schemes are not adequate for realistic data-sharing scenarios as they do not consider time constraints or only partially address them, making these mechanisms unsuitable for SSE schemes. To address this issue, we first highlight the importance of time constraints in multi-user SSE and propose a completely time-constrained SSE scheme under a two-server model. By taking advantage of the Lagrange interpolation and pre-computation, our proposed scheme enables searching over time-related encrypted data with owner-enforced time constraints. Additionally, we employ the blinding technique with the assistance of a semi-honest time server to ensure the completeness of time constraints, which is not guaranteed in existing works. Based on the leakage function, we prove the security of our proposed scheme in the simulation-based security model. Furthermore, extensive experiments demonstrate the practicality of our scheme in supporting time-constrained functions.

Privacy-preserving truth discovery (PPTD) enables the crowdsensing platform to extract reliable inferred truths from unreliable user sensory data. While mobile crowdsensing systems have driven the emergence of many applications, continuously extracting inferred truths of evolving objects over streaming data (continuous PPTD) remains a challenge. Most existing works focus on static scenarios and cannot handle the new challenges in continuous PPTD, such as accuracy decrease, user dynamics, real-time requirements, and outliers. To address these challenges, we present PTET, a PPTD framework for continuous PPTD. By mining evolving patterns, PTET extracts accurate inferred truths of evolving objects even when some epochs lack sufficient user sensory data. PTET ensures the privacy of both users and data requesters while achieving high accuracy. Furthermore, we present PTET-P for practical applications. It employs a virtual user combined with evolving patterns to effectively eliminate the impact of user dynamics in continuous PPTD. Meanwhile, PTET-P achieves “immediate on-arrival processing” to improve real-time performance significantly. In addition, we address the outliers problem with the help of evolving patterns. We provide security analysis to prove that our frameworks protect the privacy of both users and data requesters. Extensive experiments demonstrate that our frameworks dramatically outperform the existing schemes in extracting inferred truths of evolving objects in continuous PPTD.

Crowdsourcing has emerged as an effective paradigm for large-scale data collection and statistical analysis. However, the paramount concern about worker privacy has driven the development of privacy-preserving statistical analysis methods. We propose PSAC, a novel framework that leverages histograms to facilitate privacy-preserving statistical analysis in crowdsourcing. PSAC integrates secure statistical analysis protocols based on homomorphic encryption and secure two-party computation, addressing the limitations of a single cryptographic technique. It introduces innovative algorithms using histograms for statistical operations, including functions such as quantile estimation, outlier elimination, contingency table construction for$\chi ^{2}$test, and the Mann-Whitney$U$test. These algorithms exhibit minimal overhead growth with respect to data volume, demonstrating exceptional scalability for large numbers of data. Moreover, through a key-separation design, PSAC ensures that only the requester can decrypt the final results independently, even if the ciphertexts of data are exposed. Comprehensive evaluations validate the security, efficiency, and scalability of the PSAC framework.

Radio sensing has emerged as a promising solution for monitoring vital signs in a contactless manner. However, most of the existing designs focus on stationary target and struggle with body motion interference. While some efforts have been made to address this issue, the lack of a physical explanation for the motion elimination principle makes them work as a blind signal separation way and thus leaves the body motion elimination problem still as an open challenge. In this paper, we reveal for the first time the existence of “dark pixels”–specific points on the same rigid body parts that share the same body movement but exhibit varying physiological motions, with these variations still preserving the physiological rhythm. By exploiting the inherent relationship between the dark pixels, we propose a cooperative sensing framework, Co-Sense, that can achieve robust radio sensing for non-stationary targets in an explainable way. Through extensive experiments, Co-Sense demonstrates its superiority over existing methods, achieving effective motion cancellation and breath sensing with a median absolute respiratory rate (RR) error of 0.36 respiration per minute (RPM) and breath wave correlation of 0.61 under non-stationary scenarios. The results indicate the great potential of Co-Sense in enhancing the accuracy of vital sign sensing with radio signals, especially in real-world environments where targets are rarely stationary.

Recent advancements have showcased the potential of handheld millimeter-wave (mmWave) imaging, which applies synthetic aperture radar (SAR) principles in portable settings. However, existing studies addressing handheld motion errors either rely on costly tracking devices or employ simplified imaging models, leading to impractical deployment or limited performance. In this paper, we present IFNet, a novel deep unfolding network that combines the strengths of signal processing models and deep neural networks to achieve robust imaging and focusing for handheld mmWave systems. We first formulate the handheld imaging model by integrating multiple priors about mmWave images and handheld phase errors. Furthermore, we transform the optimization processes into an iterative network structure for improved and efficient imaging performance. Extensive experiments demonstrate that IFNet effectively compensates for handheld phase errors and recovers high-fidelity images from severely distorted signals. In comparison with existing methods, IFNet can achieve at least 11.89 dB improvement in average peak signal-to-noise ratio (PSNR) and 64.91% improvement in average structural similarity index measure (SSIM) on a real-world dataset.

Self-supervised learning (SSL) is a powerful approach that learns general semantic representations from large-scale unlabeled data to make downstream tasks solve easier, offering significant potential in enhancing downstream performance and alleviating the appetite for large-scale annotated data. However, existing SSL techniques, predominantly designed for natural images, may be prone to shortcuts when applied to RF signals. This study presents surprising empirical findings showing that SSL can indeed learn meaningful RF representations by employing simple group shuffle (GS) and asymmetry augmentation techniques. The GS augmentation is inspired by blind calibration tasks in Time-Interleaved Analog-to-Digital Converters (TIADC). By treating the original RF signal as a composite output from sub-ADCs, GS augmentation enriches RF signals while preserving their global semantics. We also provide a theoretical validation of the GS augmentation’s singular value consistency. Notably, we observe that the shortcut is essentially a domain gap between the pre-trained and the downstream task models. This issue can be mitigated by an asymmetry augmentation technique, which maximizes the similarity between an original RF signal and its augmented version, rather than between two augmentations of the same RF signal. By integratinggroupshuffle andasymmetryaugmentation (GSAA) into an existing contrastive learning framework, we develop an effective contrastive learning approach for RF signals. Our evaluations, spanning seven downstream RF sensing tasks across two general RF devices (WiFi and radar), strongly demonstrate that GSAA plays a significant role in advancing SSL-based solutions in RF sensing.

The high mobility in Vehicular Ad-hoc Networks (VANETs) significantly affects the reliability of data transmission. To solve this problem, Named Data Networking (NDN)-based VANETs are proposed, utilizing in-network caching and named-based forwarding to overcome the dual challenges of mobility and connectivity. Although in-network caching enhances content availability, a strategy that accurately locates and efficiently utilizes the cached content in VANETs with highly dynamic environments is still lacking. In this paper, we propose a novel NDN-based cache-aware adaptive forwarding (CAAF) strategy for VANETs. CAAF proactively predicts content locations and ensures reliable content retrieval by adaptively selecting forwarding nodes that prioritize fast delivery and stable transmission. Specifically, we design a content information table for each vehicle to record information about the Interest packets it receives. Furthermore, these tables are updated periodically across all vehicles and a prediction model is used to predict real-time in-network caching during the update interval. Subsequently, we execute a filter mechanism to sieve candidate forwarding vehicles that satisfy both the accessibility and stability requirements. These candidates are then evaluated using a multi-attribute decision-making method across diverse parameters to determine the optimal forwarding node. Our extensive simulation results demonstrate that the proposed CAAF outperforms the state-of-the-art forwarding strategy regarding content retrieval delay and Interest satisfaction ratio across diverse scenarios.

In the above article [1], on page 13900, right column, there is an empty reference citation “[?]” in the sentence “By applying Model-Agnostic Meta-Learning (MAML) to fingerprint localization, MetaLoc [?] enables the model to quickly adapt to new environments based on the obtained meta-parameters, thus reducing human labor costs.” The missing reference is listed below as [2].

Human tracking plays a crucial role in various wireless sensing applications. However, recent advancements have primarily focused on constrained experimental scenarios with less interference, often involving a few individuals performing actions in an empty space without obstacles. In empirical unconstrained scenarios, such as daily office scenes, severe interference and attenuation caused by chaotic environments is inevitable which results in dramatic performance degradation. In this paper, we introduce TBDNet, which incorporates tracking-before-detect (TBD) from conventional signal processing into learning-based models, achieving impressive tracking performance in unconstrained scenarios. TBDNet follows first-track-then-detect pipeline. It maps input heatmap sequence into high-level frame-wise features to adapt the time-varying intensity distribution and motion pattern of targets. After that, the temporal information is accumulated in feature space to obtain trace proposals. We then predict the accurate positions and probability of traces at each timestamp. To assess the efficiency of TBDNet, we collect and release the first RF-UNIT (RF-based Unconstrained Indoor Tracking) dataset, which comprises 4,030,880 radar heatmaps and the corresponding tracking annotations under 6 different scenarios. To our knowledge, RF-UNIT is the first dataset for RF-based human tracking in unconstrained scenes. We anticipate that TBDNet and the RF-UNIT dataset will significantly contribute to the advancement of RF-based sensing technologies.

Masked image modeling (MIM) has been considered as the state-of-the-art (SOTA) self-supervised learning (SSL) technique in terms of visual pretraining. The impressive generalization ability of MIM also paves the way for the remarkable success of large-scale vision foundation models. In this article, we further discuss the validity and advantages of implementing MIM techniques in the reproducing kernel Hilbert spaces (RKHSs) and we associate the analysis with a novel MIM method named R-MIM (short for RKHS-MIM). Through the careful construction of an augmentation graph and by using spectral decomposition techniques, we establish a systematic theoretical understanding between the proposed R-MIM's generalization ability and the choice of kernel function used during training. Specifically, we reach a conclusion that both of the local Lipschitz constant of the resultant R-MIM model and the corresponding expected pretraining error can have a strong composite effect on bounding downstream task error, depending on the kernel options. We demonstrate that under mild mathematical assumptions, R-MIM method is guaranteed to return a lower bound on downstream tasks in comparison to vanilla MIM techniques, such as masked autoencoder (MAE) and SimMIM. Empirical justification well corroborates our theoretical hypothesis and analysis in showing the superior generalization of the proposed R-MIM and the theoretical link to kernel choices. The code is available at: https://github.com/yurui-q/R-MIM.

Multipath TCP (MPTCP) has gained popularity to enhance data transmission. From the last decade, proposed MPTCP enhancement schemes for congestion control, path management, and packet scheduling, have been used to benefit transmission performance. However, despite their efforts, they are exigent with a comprehensive understanding of real-world performance to guide the implementation of MPTCP to a more complex wireless network. To that end, we conduct a measurement-driven study of MPTCP enhancement schemes, providing insights and in-depth demonstrations of their performance with a comprehensive real-world platform. Our finding indicates that the enhancement schemes struggle to consistently maintain high performance at all times. One can achieve optimal efficiency in its specific scenarios, but suffers extreme degradation at times. To eliminate this transmission uncertainty in wireless networks, we further propose an adaptive control framework OLSch to integrate different schemes, emphasizing their strengths to provide consistently high performance. To be specific, OLSch is implemented with different scheduling schemes and leverages an online-learning-driven approach to choose one that best fits the current network conditions. Evaluations show that OLSch obviously improves the stability of transmission in harsh network scenarios, eliminates performance degradation, and increases the 95% tail throughput by 1.45×-2.39×.

Congestion control (CC) is crucial in data center networks (DCNs), providing high throughput and low latency transmission services for diverse applications. Existing CC schemes typically rely on iterative rate adjustment at ends, and suffer from performance issues such as slow convergence, throughput fluctuations, and fairness defects. Explicit rate control (ERC) promises to address these challenges by allowing switches to directly allocate rates for each flow, freeing senders from heuristic detection of available bandwidth. However, current ERC-based schemes employ inefficient feedback control to regulate the allocated rates, resulting in sub-optimal performance. In this paper, we propose RGuide, a fast and accurate CC scheme based on ERC. RGuide can calculate accurate fair share rates in real-time at switches with the consideration of low latency, and utilize the rate to guide host adjustments instead of the need for end-to-end iteration processes. We meticulously design the ERC trigger conditions, enabling switches to recognize the different congestion states of flows and rectify flows that deviate from the fair share rate at sub-RTT timescales. We conduct actual testbed experiments and extensive simulations to evaluate RGuide comprehensively. The results demonstrate the significant advantages of RGuide in terms of convergence speed, throughput stability, and fairness.

Quantum Key Distribution (QKD) technology leverages its inherent security advantages to ensure information-theoretic security for data transmission in networks. However, existing QKD networks still face critical challenges, including network congestion that stems from limited key resources and uneven resource allocation methods. Thus, in this paper, we focus on the issue of network congestion caused by bottleneck links and aim to achieve load balancing. Considering the limited key resources, we first introduce the key resource utilization ratio as an indicator of bottleneck links and formulate the resource allocation problem as an Integer Linear Programming (ILP) problem. To deal with the complexity of the ILP problem, especially in large-scale network scenarios, we design a heuristic algorithm that can obtain near-optimal solutions within polynomial time. Finally, we implement the proposed key resource allocation scheme in various real-world network topologies using a full-stack quantum network simulator. Compared to the existing algorithms, extensive results show that our method can reduce key resource consumption by up to 50% on bottleneck links and improve the robustness of QKD networks when facing burst quantum key agreement requests.

The entanglement transport protocol with a connection-oriented mode ensures the reliable distribution of remote entanglement by reserving dedicated resources on the selected path for users in a quantum network. In most existing protocols, entanglement generation and resource allocation operate with the support of global network-synchronized time slot. However, such synchronization in a large-scale quantum network is challenging, and the idealized time slot model is not conducive to continuous and concurrent requests. Meanwhile, different link performance in memory capacity and entanglement generation rate brings out critical issues, such as long distribution delay and low resource utilization, which has not been adequately addressed by the existing protocols relying on a heuristic adoption of TCP-like transport modes. In light of these observations, we propose a dynamic rate matching-based entanglement transport protocol called DRM-ETP, which allocates different memory units on each link along an entanglement distribution path. Moreover, DRM-ETP incorporates periodic forward and backward interactions to implement fine-grained feedback and a dynamic memory allocation based on priority differentiation. These mechanisms mitigate congestion and unfairness arising from resource contention among burst requests on shared links. Extensive simulation results demonstrate that DRM-ETP significantly outperforms the existing protocols in terms of throughput and resource utilization, with less distribution delay and higher fidelity. Moreover, DRM-ETP exhibits rapid and fair convergence when handling burst requests. Our study opens up possibilities for deploying efficient entanglement transport in quantum networks, thereby holding the promise of enhanced compatibility and novel functionality.

Quantum key distribution (QKD) networks can provide information-theoretically secure key distribution between distant end nodes through key relaying. In QKD networks, the key relay protocol is vital since it provides the coordination specifications between nodes for key relaying and thus directly determines the performance, especially as the network scale expands. However, most existing protocols adopt a synchronous contend-and-relay approach, where the contention and consumption of quantum keys occur simultaneously, neglecting the storable nature of quantum keys and presenting significant challenges in reliability and quantum key utilization. To tackle these challenges, in this paper, we propose an asynchronous key relay protocol (AKRP). AKRP considers the storable nature of quantum keys, and adopts a reserve-then-relay approach to achieve lossless and zero-queuing key relaying through precise management of quantum keys and requests. On this basis, to further improve the performance of the proposed AKRP, we design two enhanced mechanisms, i.e., collision detection and resolution mechanism and multipath routing extension. The former enhances the consensus efficiency of AKRP and provides fine-grained key utilization on each link, and the latter utilizes quantum keys on possible relay paths and thus effectively copes with quantum key exhaustion. By conducting extensive experiments on a semi-physical real QKD network platform, results demonstrate that AKRP is superior to existing schemes in terms of end-to-end key throughput, quantum key consumption, and relaying latency.

Real-time video streaming requires high throughput and low delivery time for enhanced user’s Quality of Experience (QoE). This motivates the use of multi-path transmission to improve performance. However, ensuring target performance within specified deadlines and priorities for video frames is particularly crucial for real-time communication and video quality, especially in scenarios with limited resources. To address this challenge, we propose a novel framework,vStreamPth, to guarantee high-quality real-time video streaming through multi-path transmission. For essential quality assurance,vStreamPthincorporates key requirement indicators that guide the transmission decisions of video frames across predefined multiple paths. In this framework, lightweight and robust decision-making is achieved through the collaboration of application-oriented and network-oriented data scheduling. Specifically, it employs robustness estimation to maintain the non-blocking delivery of frames, and further applies online fine-tuning to correct variations caused by changes in end-to-end transmission and multi-path network conditions. We implement a prototype ofvStreamPthin Linux user space and conduct a thorough evaluation. Experimental results demonstrate the absolute improvement ofvStreamPthin achieving high QoE and deadline satisfaction ratio compared to existing multi-path solutions.

A Link Flooding Attack (LFA) is a type of link-aimed Distributed Denial of Service (DDoS) attack that can overwhelm the Internet critical links to cut off connections with lots of low-rate, seemingly benign traffic. To defend against such threats, a promising solution involves mitigating the attack through load balancing. However, adaptive attacks employ two effective means to circumvent existing load balancing strategies. The first is the frequent changing of targets, known as rolling attacks. Rolling attacks exploit the delay between attack detection feedback and the mitigation of load balancing, depleting the defender’s resources. The second is the strategical selection of target links to create the worst-case scenario for load balancing algorithms. To address these challenges, we propose LinkDam. Specifically, LinkDam adopts a proactive approach by tracking and predicting potential victim links, providing defense against all targets of rolling attacks. Subsequently, we introduce a robust load balancing strategy to prevent the exploitation of selected link combinations. Additionally, LinkDam introduces a partial deployment approach, demanding a mere 40% of nodes be programmable (i.e., SDN nodes) while maintaining an acceptable 10% performance reduction from the maximum achievable. The experimental results indicate that LinkDam surpasses an 80% accuracy threshold, and exhibits a 57% higher tolerance to attack budgets compared to state-of-the-art solutions.

Link-flooding attacks (LFAs) pose a significant challenge to Internet availability by attacking critical network links with high volumes of seemingly legitimate traffic. In response, researchers have developed network topology obfuscation (NTO) to safeguard critical links. However, state-of-the-art NTO defenses are coarse-grained, leading to less efficient security and usability. In addition, once under attack, NTO schemes cannot identify the attacker’s bot and launch counter-defensive measures. To address these issues, this paper introduces SpiderNet, which employs advanced obfuscation techniques to secure critical links while using strategically created honeypot links for effective bot identification. When adversaries probe the network, SpiderNet captures their probing behavior and deliberately feeds back misinformation about honeypot links. By analyzing the attack patterns directed at these decoy targets, SpiderNet correlates them with adversarial probing activities to effectively identify the bots. Our experiments demonstrate that SpiderNet is more robust than state-of-the-art NTO schemes in terms of security and usability, while also being capable of identifying LFA bots.

Quantum networks, which work by establishing entanglement between distant quantum end nodes (known as end-to-end entanglement distribution), are the promising infrastructure for quantum applications. However, the inherent loss in quantum channels and quantum decoherence contribute to the scarcity of entanglement resources in quantum networks. Consequently, there is an inevitable gap between available entanglement resources and requests’ demands, significantly hindering concurrent end-to-end entanglement distributions. In this paper, we present NarrowGap, an end-to-end entanglement distribution design that can alleviate the negative impact of entanglement resource scarcity on the request service capability of quantum networks. At the heart of NarrowGap, the resource transfer scheme (RTS) is designed to transfer idle entanglement resources to boost the bottlenecks’ capacities based on the unique feature of entanglement swapping, thus narrowing the gap between available entanglement resources and requests’ demands for end-to-end entanglements. Besides, NarrowGap presents a resource allocation scheme (RAS) to guarantee fairness, considering both the success probability of end-to-end entanglement distribution and each request’s demand, to address resource competition in bottlenecks. Extensive simulations demonstrate that NarrowGap outperforms three representative schemes and can achieve more than twice the performance improvement in request service rate.

Named Data Networking (NDN) has emerged as a promising network paradigm for the future Internet. It revolutionizes content retrieval by decoupling it from specific locations, thereby overcoming the limitations of traditional IP addressing and significantly enhancing data delivery efficiency. Additionally, NDN’s stateful forwarding plane for routers enables robust aggregation of identical requests, bolstering resistance against Distributed Denial of Service (DDoS) attacks. Despite these advancements, NDN remains vulnerable to the Interest Flooding Attack (IFA), wherein excessive requests from attackers can compromise transmission quality by depleting router resources. In the current landscape, researchers have proposed various strategies aimed at improving the accuracy, timeliness, and cost-effectiveness of defenses against IFA attacks, presuming stable user behavior. However, several challenges persist in effectively countering IFA attacks, including the need to ensure transmission quality throughout users’ lifecycles, eliminate attacks at their origin, and adapt to dynamic user behaviors. In response to these challenges, this paper presents the Hybrid Proactive Reactive Defense Scheme (HPR-DS). HPR-DS employs distinct proactive and reactive modules for resource management and user behavior analysis, respectively, at intermediate and edge nodes. It employs time series analysis to gauge evolving resource requirements and maintains separate resource pools for each content. Additionally, HPR-DS utilizes multidimensional data clustering to accurately identify attackers. Simulation results demonstrate the superior performance of HPR-DS in safeguarding user transmission quality throughout the entirety of their lifecycle and in enhancing detection precision in dynamic network environments.

Diffusion models have recently brought a powerful rev-olution in image generation. Despite showing impressive generative capabilities, most of these models rely on the current sample to denoise the next one, possibly resulting in denoising instability. In this paper, we reinterpret the iterative denoising process as model optimization and leverage a moving average mechanism to ensemble all the prior samples. Instead of simply applying moving average to the denoised samples at different timesteps, we first map the denoised samples to data space and then perform moving average to avoid distribution shift across timesteps. In view that diffusion models evolve the recovery from low-frequency components to high-frequency details, we fur-ther decompose the samples into different frequency components and execute moving average separately on each component. We name the complete approach “Moving Aver-age Sampling in Frequency domain (MASF)”. MASF could be seamlessly integrated into mainstream pre-trained dif-fusion models and sampling schedules. Extensive experi-ments on both unconditional and conditional diffusion mod-els demonstrate that our MASF leads to superior performances compared to the baselines, with almost negligible additional complexity cost.

This research focuses on the issue of single-image reflection removal (SIRR) in real-world conditions, examining it from two angles: the collection pipeline of real reflection pairs and the perception of real reflection locations. We devise an advanced reflection collection pipeline that is highly adaptable to a wide range of real-world reflection scenarios and incurs reduced costs in collecting large-scale aligned reflection pairs. In the process, we develop a large-scale, high-quality reflection dataset named Reflection Removal in the Wild (RRW). RRW contains over 14,950 high-resolution real-world reflection pairs, a dataset forty-five times larger than its predecessors. Regarding perception of reflection locations, we identify that numerous virtual reflection objects visible in reflection images are not present in the corresponding ground-truth images. This observation, drawn from the aligned pairs, leads us to conceive the Maximum Reflection Filter (MaxRF). The MaxRF could accurately and explicitly characterize reflection locations from pairs of images. Building upon this, we design a reflection location-aware cascaded framework, specifically tailored for SIRR. Powered by these innovative techniques, our solution achieves superior performance than current leading methods across multiple real-world benchmarks. Codes and datasets are available at here.

Neural trojans constitute a serious threat to systems that employ neural networks. In response to this threat, a multitude of trojan defense strategies have surfaced, with model-level measures, particularly those applied post-training, showcasing broader applicability. However, many such model-level defenses could be vulnerable because their robustness against adaptive attacks launched by sophisticated adversaries is unvalidated. In this paper, we introduce AdvNets, a general framework developed from the perspective of adversarial attacks, to demonstrate the vulnerability and enhance the robustness of model-level trojan defenses against adaptive attacks. Specifically, we implement feature-based and score-based attack modules that thoroughly circumvent multiple state-of-the-art defenses without modifying the so-called defendable backdoor patterns. To counteract these vulnerabilities, we craft an independent random decision envelopment mechanism where detections made by our detectors are mutually independent. The implementation of this mechanism markedly improves the AUC score for detecting adversarial models to over 80%. This presents a strong argument for designing a robust and dependable trojan defense system.

Fall detection using Radio Frequency (RF) signals with deep learning has exhibited significant promise in recent years. However, the costly collection of RF data with falls has hampered the performance of existing methods. While there has been approaches which can generate RF signals using various simulation methods, they rely on human-body modeling based on other modalities. Moreover, the realism of the generated signals is insufficient because these approaches cannot accurately capture the human radar cross section (RCS). In this paper, we propose SimFall, which generates simulated data for RF-based fall detection without overhead for data collection. SimFall first simulates the fall process by manipulating the human body mesh based on practical fall model. Then a grid shooting and bouncing ray (SBR) method is utilized to calculate the accurate RCS. Finally, SimFall computes the original signal and transforms it into different forms that reveal the features of falls. The experimental results demonstrate that the data produced by SimFall effectively enhances the accuracy of the RF-based fall detection network.

Recent advancements have showcased the potential of hand-held millimeter-wave (mmWave) imaging, which applies synthetic aperture radar (SAR) principles in portable settings. However, existing studies addressing handheld motion errors either rely on costly tracking devices or employ simplified imaging models, leading to impractical deployment or limited performance. In this paper, we present IFNet, a novel deep unfolding network that combines the strengths of signal processing models and deep neural networks to achieve imaging and focusing for handheld mmWave systems. By integrating multiple priors and mapping the optimization processes into an iterative network structure, IFNet effectively compensates for phase errors and recovers high-fidelity images from severely distorted signals. Extensive experiments demonstrate that IFNet outperforms state-of-the-art methods, both qualitatively and quantitatively.

Radar sensing has been a promising solution for contactless monitoring of Heart Rate Variability (HRV), an essential indicator of the cardiovascular and autonomic nervous systems. However, existing works neglect heartbeat-driven body surface motions spreading across the entire body with spatial variations, which limits their accuracy in identifying fine-grid consecutive heartbeat timings and overall HRV performance. In this paper, we propose to exploit the entire body reflections and model the inherent spatial-temporal relationship between these reflections and heartbeats by deep neural network for contactless HRV monitoring. Specifically, a hybrid convolution-transformer-based network is designed to convert the complex multi-dimensional spatial-temporal modeling problem into an efficient sequence modeling process. Experimental results demonstrate its superiority over the baseline method, achieving the median IBI estimation error of 12ms (w.r.t. 98.47% accuracy), RMSDD error of 7.3ms, SDRR error of 2.9ms, pNN50 error of 5.5%.

The mutual interference mitigation between identical or similar radar systems in autonomous driving has gained wide spread attention from both academia and industry. The resulted ghost target interference will reduce the sensitivity of the radar sensor and increase the false alarm rate. To tackle this problem, in this paper, we make full use of two characteristics of interference to achieve ghost target interference mitigation in the Doppler domain. The key insight lies in the fact that the interference is one-way propagation, and thus the resulted ghost target can be converted to the noise floor in the Doppler domain through random slow-time coding. Moreover, the high power characteristic of interference allows us to further enhance the interference mitigation performance by adopting a signal-to-interference-plus-noise ratio (SINR) maximization principle. Numerical examples are provided to demonstrate the effectiveness of the proposed interference mitigation approach.

This paper introduces a novel paradigm for learning-based RF sensing, termed Pre-training RF signals In Sparsity-aware Masked autoencoders (PRISM), which shifts the RF sensing paradigm from supervised training on limited annotated datasets to unsupervised pre-training on large-scale unannotated datasets, followed by fine-tuning with a small annotated dataset. PRISM leverages a carefully designed sparsity-aware masking strategy to predict missing contents by masking a portion of RF signals, resulting in an efficient pre-training framework that significantly reduces computation and memory resources. This addresses the major challenges posed by large-scale and high-dimensional RF datasets, where memory consumption and computation speed are critical factors. We demonstrate PRISM’s excellent generalization performance across diverse RF sensing tasks by evaluating it on three typical scenarios: human silhouette segmentation, 3D pose estimation, and gesture recognition, involving two general RF devices, radar and WiFi. The experimental results provide strong evidence for the effectiveness of PRISM as a robust learning-based solution for large-scale RF sensing applications.

Load balancing in data center networks (DCNs) is a crucial and complex undertaking. Multi-path TCP (MPTCP) has been proposed as a cost-effective solution that aims to distribute workloads and improve network resource utilization. However, it can escalate buffer occupancy and undermine burst tolerance, particularly in scenarios involving incast short flows. To address these limitations, we propose a novel multi-path congestion control algorithm, RateMP, to optimize bandwidth utilization efficiency while ensuring burst tolerance in DCNs. RateMP employs a hybrid window and rate control loop with coupled gradient projection adjustment, enabling fast and fine-grained bandwidth allocation and accelerating convergence. Additionally, RateMP eliminates the limitation of cwnd with under-rate pacing to protect incast and busty flows. We prove that RateMP is Lyapunov stable and asymptotically stable, and show the improvement of RateMP through a kernel-based implementation and extended large-scale simulations. RateMP keeps high bandwidth utilization, cuts RTT by 2x and reduces flow completion times (FCT) by 45% in incast scenarios compared to existing algorithms.

Remote entanglement distribution in an efficient and reliable manner, especially in the context of a large-scale quantum network with multiple requests, remains an unsolved challenge. The key difficulties lie in achieving spontaneous and precise control over the entanglement distribution procedure, as multiple nodes need to reach a consensus on how to perform it. From the network aspect, allocating link-layer entangled pairs as resources to achieve high efficiency is also challenging. To address these issues, we propose a decentralized Reliable Entanglement Distribution Protocol (REDP) for large-scale networks. The protocol operates in a Forward-Backward Propagation (FBP) manner, where consensus is reached hop-by-hop and disseminated to all nodes on the path. We further use probabilistic analysis and quasi-static modeling to seek the fairness and efficiency of the network based on the above transmission model. Accordingly, we introduce a Source Window Strategy (SWS) and an Entanglement Allocation Strategy (EAS) to assign sending windows and allocate resources for multiple requests, ensuring a high level of fairness and efficiency from a network perspective. Through systematic simulations involving both classical and quantum communication protocols, we demonstrate that REDP outperforms existing approaches in terms of fairness, throughput, and fidelity performance.

Set-valued data are commonly used to represent subsets of a universal set and are frequently utilized in online services, such as online shopping preferences, website browsing records, and recently visited places. By collecting set-valued data from users, service providers can perform statistical analysis to obtain a joint distribution of service usage data and subsequently learn the association between different kinds of set-valued data to improve the quality of service. However, collecting set-valued data raises privacy concerns about the potential misuse of records to infer individuals’ identities and preferences. Although some privacy-preserving aggregation mechanisms for set-valued data have been proposed, they have not yet achieved joint distribution analysis with high accuracy. In this paper, we propose a joint distribution analysis method for set-valued data with local differential privacy (LDP). We design a scalable perturbation mechanism under$\epsilon $-LDP by limiting the range of users’ responses in the collection process and cyclically shifting the set-valued data in an encoded uniform format, ensuring that the size of the universal set does not influence the accuracy of the results. Based on the perturbation method, we develop an analysis method to efficiently obtain association information between two sets. By performing specific bitwise operations on the perturbed data matrices, the computational overhead is linear with respect to the cardinality of the item set. In addition to theoretically analyzing the error bound and proving the security of our work, extensive experimental results on synthetic and real-world datasets demonstrate that our scheme achieves better utility than existing state-of-the-art approaches.

Federated Learning (FL) enables multiple distributed clients to collaboratively train a model with owned datasets. To avoid the potential privacy threat in FL, researchers propose the DP-FL strategy, which utilizes differential privacy (DP) to add elaborate noise to the exchanged parameters to hide privacy information. DP-FL guarantees the privacy of FL at the cost of model performance degradation. To balance the trade-off between model accuracy and security, we propose a differentially private federated learning scheme with an adaptive noise mechanism. This is challenging, as the distributed nature of FL makes it difficult to appropriately estimate sensitivity, where sensitivity is a concept in DP that determines the scale of noise. To resolve this, we design a generic method for sensitivity estimates based on local and global historical information. We also provide instances on four commonly used optimizers to verify its effectiveness. The experiments on MNIST, FMNIST and CIFAR-10 convincingly prove that our proposed scheme achieves higher accuracy while keeping high-level privacy protection compared to prior works.

The electrocardiogram (ECG) has always been an important biomedical test to diagnose cardiovascular diseases. Current approaches for ECG monitoring are based on body attached electrodes leading to uncomfortable user experience. Therefore, contactless ECG monitoring has drawn tremendous attention, which however remains unsolved. In fact, cardiac electrical-mechanical activities are coupling in a well-coordinated pattern. In this paper, we achieve contactless ECG monitoring by breaking the boundary between the cardiac mechanical and electrical activity. Specifically, we develop a millimeter-wave radar system to contactlessly measure cardiac mechanical activity and reconstruct ECG without any contact in. To measure the cardiac mechanical activity comprehensively, we propose a series of signal processing algorithms to extract 4D cardiac motions from radio frequency (RF) signals. Furthermore, we design a deep neural network to solve the cardiac related domain transformation problem and achieve end-to-end reconstruction mapping from RF input to the ECG output. The experimental results show that our contactless ECG measurements achieve timing accuracy of cardiac electrical events with median error below 14ms and morphology accuracy with median Pearson-Correlation of 90% and median Root-Mean-Square-Error of 0.081mv compared to the groudtruth ECG. These results indicate that the system enables the potential of contactless, continuous and accurate ECG monitoring.

While deep learning-based RF perception has received significant attention in recent years, the requirement for massive labeled RF data has hindered its further advancement. Despite existing efforts in synthesizing signals, they fail to accurately calculate the Radar Cross Section (RCS) of the target, leading to less practicality of the synthesized signals. In this paper, we introduce Simulated Body Radio Frequency (SBRF), a novel signal synthesis framework for calculating more realistic RCS by combining ray tracing with electromagnetic computation. SBRF involves three key components: a grid-based Shooting and Bouncing Ray (SBR) algorithm to calculate fine-grained human body RCS, a novel ray partitioning algorithm to improve the efficiency of ray tracing, and a coordinate transformation method to sense moving targets. Furthermore, we also design unique data augmentation techniques to improve the efficiency and generalizability of signal synthesis. Extensive experimental evaluations conducted on two publicly available datasets, involving wide-scale activity recognition and fine-grained gesture recognition, demonstrate the effectiveness of SBRF-generated signals in improving RF perception performance and alleviating the challenge of RF data collection.

As a promising complement to terrestrial cellular networks, satellite networks have recently drawn increasing attention, offering seamless coverage cost-effectively. However, with the rapidly increasing users' demand for multimedia content, how to achieve efficient content transmission seamlessly becomes a critical but knotty problem. To provide an efficient solution from the routing perspective, in this paper, we propose an opportunistic content-aware routing scheme. Our scheme combines the features of in-network caching and content awareness of information-centric networking (ICN) architecture. The basic idea of the proposed scheme is to sense users' requests and find the optimal route solution with the largest potential gain. Moreover, considering the limitation of real-time signaling collection in satellite networks, we design a cached content prediction method. The method is capable of inferring the probability of content being cached based on historical popularity information, providing essential information for measuring potential gains. Extensive simulation results demonstrate that the proposed opportunistic content-aware routing scheme outperforms baseline approaches with significantly reduced delay and traffic consumption.

WiFi-based indoor localization has gained widespread attention due to the pervasive availability of WiFi Access Points (APs). While signal processing-based methods can achieve decimeter-level localization, their performance is constrained by the limited spatial resolution of WiFi systems, especially in complex environments with strong interference. By contrast, deep learning-based methods have achieved impressive performance even in complex environments, which however often fail to generalize to new environments. In this paper, we propose a novel framework to learn domain-invariant model for WiFi-based indoor localization, which maintains impressive performance across different environments. The key insight is to design a deep learning-based WiFi localization system through the perspective of signal processing. Specifically, we let the neural network estimate APs-centered polar coordinates to avoid fitting the coordinates of APs strongly correlated with the environment, enabling us to obtain the domain-invariant model. To unleash the potential of neural networks in regressing high-precision parameters, we design a beamforming layer to integrate the knowledge of signal processing. Furthermore, we propose a multi-task learning scheme to further improve localization accuracy. Extensive experiments on diverse datasets have demonstrated that the localization performance of our method outperforms state-of-the-art methods and demonstrates superiority under cross-domain conditions.

WiFi-based respiration sensing technology has gained increasing attention due to its contactless sensing capabilities and utilization of existing WiFi devices. However, existing studies are limited to certain scenarios without addressing the motion interference from other individuals. In this paper, we tackle the challenge of robust respiration sensing in the presence of other individuals. Specifically, through an in-depth examination of the correlation between respiratory signals and spatial beam patterns, we develop a respiratory-energy based approach to evaluate the diverse impact of dynamic interference on respiratory signals. When significant interference is detected, we employ a convex-optimization-based beam control strategy, which exploits the inherent characteristics of human respiration, to adaptively adjust the spatial beam pattern. This approach enables a robust and precise gain adjustment between the target and interfering individual, effectively mitigating the impact of interference. Experimental results demonstrate that our approach can reduce the mean absolute error (MAE) of respiration detection by up to 32% compared to state-of-the-art methods, significantly enhancing the accuracy and robustness of WiFi-based respiration sensing.

Millimeter-wave (mmWave) radar has been widely used in wireless sensing due to its non-contact nature, privacy preservation, and immunity to adverse lighting conditions. However, with more and more mmWave radars working in the same frequency band, mutual device interference among them is inevitable and has become a serious problem. The device interference reduces the signal-to-interference-plus-noise ratio (SINR) and significantly degrades the detection performance. Existing works mainly focus on the vital sign monitoring in different practical scenarios (e.g., device movement, human movement, multi-person interference, and in-car scenario), and the vital sign monitoring in the presence of mutual device interference is still not well resolved. In this paper, we propose a novel interference mitigation framework, iSense, to enable radar vital sign sensing under device interference. By exploiting one-way propagation characteristic of device interference, iSense can effectively detect and suppress the interference. We evaluate iSense under a variety of complex device interference scenarios, including different distances, angles, and numbers of aggressor radars, as well as the impact of different environments. Experimental results show that the accuracy of respiration and heartbeat estimation of iSense can reach over 99.2% and 98.6%, indicating that iSense takes an important step towards the practical development of radar sensing.

Entanglement distribution between distant quantum nodes plays an essential role in realizing quantum networks’ capabilities. In addition to path selection, remote entanglement distribution involves two pivotal quantum operations, i.e., entanglement generation and entanglement swapping. The existing studies mainly adopt two methods, i.e., Tell-and-Generation (TAG) and Tell-and-Swapping (TAS), to manage these two quantum operations on a selected path. However, both methods fatally introduce redundant stop-and-wait processes, which are detrimental to the performance of remote entanglement distribution in terms of latency and fidelity. To achieve low-latency and high-fidelity entanglement distribution between far-off quantum nodes, we propose a segment-based method consisting of an entanglement generation algorithm and a segment design to diminish the unnecessary stop-and-wait processes. The entanglement generation algorithm adopts a concurrent design to establish entanglement links using the one-demand generation model, thus effectively reducing waiting time compared to hop-by-hop and parallel designs. The segment design is proposed to split a long-distance path into multiple short-haul segments with the similar ability to swap entanglement, and these segments build multi-hop entanglement connections in parallel. Extensive simulations show that the segment-based method significantly outperforms the existing methods, including TAG and TAS, in entanglement distribution latency and effectively mitigates fidelity attenuation.

With the development of datacenter networks (DCNs) towards high bandwidth and low latency, the demands of high-level datacenter applications are heading towards high performance and high reliability, which makes traffic congestion one of the most notable problems in DCNs and brings new challenges to transport protocols. Proactive transport protocols are gaining prevalence due to their ability to provide accurate feedback and precise end-to-end control, while multipath transmission is having a broader application space in the multi-path topology of large-scale DCNs. However, these advanced transport protocols aim to improve their performance by addressing some specific congestion problems, but fail to handle multiple congestion problems caused by incast, high workload and load imbalance. Their performance in terms of flow completion time (FCT), delay, robustness, and balance still has room for further improvement. In this paper, we propose ProactMP, a novel proactive multipath transport protocol for further improvement of datacenter communications. ProactMP utilizes the rich resources of parallel paths in modern DCN and spreads the load across available network paths to improve network efficiency. ProactMP deploys a credit-based bandwidth allocation strategy to achieve low delay and zero packet loss, and overcommits receiver downlinks to ensure high link utilization. We have implemented ProactMP in the Linux system. Our testbed experiments show that ProactMP outperforms the TCP variants, MPTCP variants and a leading proactive transport protocol in FCT, link utilization, fairness and latency.

The quantum network that allows users to communicate in a quantum way will be available in the foreseeable future. The network capable of distributing Bell state entangled pairs faces many challenges due to entanglement decoherence and limited network performance, especially when the network scale is enormous. Many entanglement distribution protocols have been proposed so far, and most of them are in a centralized and synchronized manner, which may be infeasible in large-scale networks. As such, in this paper, we propose a full spontaneous version of quantum networks in which the quantum nodes autonomously manage multiple entanglement distribution requests. However, one major issue is that quantum nodes have little knowledge about the network, especially the congestion (e.g., some nodes may have no usable quantum memories). We present a routing algorithm to adaptive evaluate the congestion on the neighbor nodes to avoid potential congestion. We use SimQN, the new network layer simulation platform built by our research team, to evaluate our proposed design. The result demonstrates that it can adapt to changes in network resources and reduce the drop rate that eventually leads to a higher entanglement distribution rate but remains fair for multiple requests to use the network resources fairly and achieve a more balanced throughput.

Named Data Networking (NDN) stands out as a promising Information Centric Networking architecture capable of facilitating large-scale content distribution through in-network caching and location-independent data access. However, attackers can easily inject poisoned content into the network, called content poisoning attacks, which leads to a substantial deterioration in user experience and transmission efficiency. In existing schemes, routers fail to determine the contamination source of received poisoned content, leading to the inability to accurately identify attacker nodes. Besides, attackers’ dynamic behaviors and network instability could disrupt identification results. In this paper, we propose a Secure and Lightweight scheme against content poisoning attacks based on Probing (SLP), where a proactive and reliable probing protocol is designed to identify adversaries quickly and precisely. In SLP, a router sends specifically chosen interest packets to probe a suspicious node, so that the returned corresponding content can straightly reflect its trustworthiness without other nodes’ interference. In addition, a hypothesis testing algorithm is developed to analyze the returned content, which can exclude the impact of transmission errors and adapt to dynamic attackers. Moreover, we utilize users’ feedback to avoid unnecessary probing costs on unaffected routers, with its reliability guaranteed by an efficient cuckoo-filter-based feedback validation mechanism. Security analysis shows that SLP achieves resistance against content poisoning attacks and malicious feedback. The experimental results demonstrate that SLP makes users hardly be affected by attacks and brings in only slight overhead.

Named Data Networking (NDN), with a receiver-driven connectionless communication paradigm, naturally supports content delivery from multiple sources via multiple paths. In a dynamic environment, sources and paths may change unexpectedly and are uncontrollable for consumer, which requires flexible rate control and real-time multi-path management, still lacking investigations. To address this issue, we propose an Adaptive Multi-source Multi-path Congestion Control (AMM-CC) scheme based on online learning. AMM-CC explores source/path distribution with continuous micro-experiments and abstracts the empirically experienced performance by meticulously designed two-level utility functions. Specifically, AMM-CC enables each consumer to optimize a local transmission-level utility function that fuses multi-source characteristics, including congestion level and source weights. Then, a sub-gradient descent method is designed to adjust transmission rate adaptively and achieve fine-grained control. Moreover, AMM-CC coordinates consumer with the forwarding module to ensure efficient and on-time multi-path management. It enables consumer to determine congestion gap among multiple paths by a path-level utility that sensitively captures changes and congestion on each path. Then, consumer further notifies the forwarding module in achieving precise traffic transferring. We conducted comprehensive evaluations in dynamic scenario with various content distribution using the NDN simulator, ndnSIM. The evaluation results demonstrate that AMM-CC can adapt to flexible content acquisition from multi-sources and significantly improve bandwidth utilization of multi-path compared with state-of-the-art schemes.

In data center networks (DCNs), numerous congestion control schemes utilize explicit congestion notification (ECN) to achieve low average queue delay. Such schemes generally mark packets based on the current queue length exceeding a marking threshold. However, due to the delay of ECN feedback, the queue length may further increase before the congestion notification is delivered to senders, which may lead to uncontrollable maximum queue length when bursts occur. In this paper, we propose an early ECN marking scheme based on prediction, E-ECN, to control the maximum queue length in DCNs. E-ECN uses predicted queue length rather than the current to indicate congestion with an advance time which offsets the hysteresis of ECN. We theoretically and experimentally demonstrate that early marking does not impact the throughput with appropriate selection of the advance time, and we provide guidelines for the selection in DCNs. Our simulation results show that E-ECN achieves shorter average queue delay and controllable maximum queue length in general with a bandwidth utilization guarantee. E-ECN greatly reduces queue overflow and improves the robustness of DCNs.

In this paper, we present RF-based Multi-view Pose machine (RF-MvP) for multi-person 3D pose estimation using RF signals. Specifically, we first develop a lightweight anchor-free detector module to locate and crop regions of interest from horizontal and vertical RF signals. Afterward, we propose a Multi-view Fusion Network to unproject the RF signals from the horizontal and vertical millimeter-wave radars into a unified latent space, and then calculate the correlation for weighted fusion. Finally, a Spatio-Temporal Attention Network is designed to reconstruct the multi-person 3D skeleton sequences, in which the spatial attention module is proposed to recover invisible body parts using non-local correlations among joints and the temporal attention module refines the 3D pose sequences using temporal coherency learned from frame queries. We evaluate the performance of the proposed RF-MvP and state-of-the-art methods on a large-scale dataset with multi-person 3D pose labels and corresponding radar signals. The experimental results show that RF-MvP outperforms all of the baseline methods, which locates multi-person 3D key points with an average error of 73mm and generalizes well in new data such as occlusion, low illumination.

Due to a large number of brand logos and similar text appearances, existing deep detection networks are prone to generating multiple overlapping logos at a single location. In this paper, we propose a two-stage approach, Cascade-LogoNet, by adding a verification stage to eliminate the classification ambiguity. We group overlapping logo proposals and verify the class predictions inside each group by learning more discriminative features with strong inter-class discrepancy. We also design a lightweight detector to improve computational efficiency and prevent over-fitting given limited training data. In addition, we propose a new data augmentation strategy, named small logo preserving stitching (SLPS), to improve the detection accuracy of small logos. When stitching training images to increase the loss ratio of small logos, we keep the original small logos on the stitched image to avoid extremely undistinguishable small ones. Our Cascade-LogoNet achieves 63.5 mAP and 64.4 mAP on the dataset QMUL-OpenLogo [1] when using ResNet-50 and ResNet-50-DCN [2] as backbone respectively, which surpasses previous methods by a large margin.

The past decade has witnessed emerging applications of breath monitoring using off-the-shelf WiFi devices owing to their low-cost, non-intrusive, and privacy-friendly characteristics. While existing works have achieved promising results in certain scenarios, the performance degradation introduced by the interfering person who moves around the target user has not been fully investigated, which hinders practical applications of WiFi-based breath sensing. In this paper, we propose a robust respiration sensing system with WiFi which could achieve accurate respiration sensing under strong interference. To achieve this, we first design a 2-D Capon beamformer to maximize the signal-to-interference-plus-noise ratio (SINR). Then, the interfering user’s trajectory is estimated through spatial-temporal processing. Finally, we design a respiration extracting algorithm based on the constraint of the interferer’s trajectory and breath energy to find the optimal position to extract breath signals. Extensive experimental results show that the proposed framework can reduce the Mean Absolute Error (MAE) of breath rate estimation by up to 48% compared with the existing state-of-the-art methods, which demonstrates the superior robustness and effectiveness of our system.

The past decade has witnessed emerging applications of achieving indoor localization using WiFi by estimating the Angle of Arrival (AoA). While various algorithms have been proposed, their performance in practical indoor environment is still limited. An important limitation lies in the fact that the confidences of AoA estimations from different devices are actually unequal, which has not been considered nor addressed. In this paper, we propose WiCo, a confidence-aware localization framework by analyzing the confidence of the spatial spectrum. Specifically, we first evaluate the unequal confidence caused by different beamwidth and multipath on different APs. Then, we propose a normalized distribution confidence and a full reference confidence to quantify the reliability of spatial spectrum on different devices. Finally, we resolve the unequal confidence factor caused by geometric deployment through re-weighting and perform localization. Extensive real-world experiments demonstrate that WiCo could reduce the median localization error by 43.8%.

Human gesture recognition using millimeter-wave (mmWave) signals provides attractive applications including smart home and in-car interfaces. While existing works achieve promising performance under controlled settings, practical applications are still limited due to the need of intensive data collection, extra training efforts when adapting to new domains, and poor performance for real-time recognition. In this paper, we propose DI-Gesture, a domain-independent and real-time mmWave gesture recognition system. Specifically, we first derive signal variations corresponding to human gestures with spatial-temporal processing. To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework for mmWave signals based on correlations between signal patterns and gesture variations. Furthermore, a spatial-temporal gesture segmentation algorithm is employed for real-time recognition. Extensive experimental results show DI-Gesture achieves an average accuracy of 97.92%, 99.18%, and 98.76% for new users, environments, and locations, respectively. We also evaluate DI-Gesture in challenging scenarios like real-time recogntion and sensing at extreme angles, all of which demonstrates the superior robustness and effectiveness of our system.

Recent advance of Intelligent Reflecting Surface (IRS) introduces a new dimension for secure communications by reconfiguring the transmission environments. In this paper, we devise a secure transmission scheme for multi-user Mutiple-Input Single-Output systems by leveraging multiple collaborative IRSs. Specifically, to guarantee the worst-case achievable secrecy rate among multiple legitimate users, we formulate a max-min problem that can be solved by an alternating optimization method to decouple it into multiple sub-problems. Based on semidefinite relaxation and successive convex approximation, each sub-problem can be further converted into convex problem and easily solved. Extensive experimental results demonstrate that our proposed scheme can adapt to complex scenarios for multiple users and achieve significant gain in terms of achievable secrecy rate. Compared to the traditional single IRS scheme, the proposed scheme can achieve better performance at the range of 2.4-6.4 bps/Hz with the increase in the number of reflecting elements in the multi-user scenarios. We also evaluate the gap between the secrecy rate for our proposed scheme under continuous phase shift/amplitude control and discrete phase shift/amplitude control, and our results show that the secrecy rate obtained from discrete approximation method converges to that achieved from the proposed scheme when increasing the discretization granularity.

In this paper, we propose a radio-assisted human detection framework by incorporating radio information into the state-of-the-art detection methods, including anchor-based one-stage detectors and two-stage detectors. We extract the radio localization and identifier information from the radio signals to assist the human detection, due to which the problem of false positives and false negatives can be greatly alleviated. For both detectors, we use the confidence score revision based on the radio localization to improve the detection performance. For two-stage detection methods, we propose to utilize the region proposals generated from radio localization rather than relying on region proposal network (RPN). Moreover, with the radio identifier information, a non-max suppression method with the radio localization constraint has also been proposed to further suppress the false detections and reduce miss detections. Experiments on the simulative Microsoft COCO dataset and Caltech pedestrian datasets show that the mean average precision (mAP) and the miss rate of the state-of-the-art detection methods can be improved with the aid of radio information. Finally, we conduct experiments in real-world scenarios to demonstrate the feasibility of our proposed method in practice.

The quantum network is designed to connect numerous quantum nodes and support various ground-breaking quantum applications. Most of these applications require communicating parties to share entangled pairs. Therefore, entanglement routing, a technology distributing entangled pairs between distant quantum nodes, plays a vital role in realizing quantum networks’ capability. However, due to the limitation of quantum memory size and quantum decoherence, the entangled pairs shared by adjacent quantum nodes can hardly satisfy concurrent entanglement routing requests, thus leading to severe network congestion. In this paper, we propose a novel congestion mitigation (CM) scheme to tackle such bottleneck problems. The basic idea of CM is to “recycle” idle link-level entanglement resources from well-resourced links to bottleneck links utilizing a unique enabling technology of quantum networks, called entanglement swapping. CM can increase the capacity of each bottleneck link, thus overcoming resource limitations to improve resource utilization and network throughput. To complete our work, we also propose a swapping-based entanglement routing design, including path selection and resource allocation algorithms. Extensive simulations show that our design can significantly alleviate network congestion and improve the request service rate of quantum networks compared to the traditional entanglement routing designs.

Data Center Networks (DCNs) suffer from synchronized bursts for network topology and parallel applications, leading to buffer overflows at switches and increasing network delay. To overcome this problem, some congestion control algorithms like DCTCP use Explicit Congestion Notification (ECN) to notify in-network congestion and reduce switch buffer occupancy. However, the traditional Additive Increase Multiplicative Decrease (AIMD) method causes high fluctuation of round-trip time (RTT) in DCNs. Some intelligent congestion control algorithms designed for Internet can achieve great flexibility, but are not applicable in DCNs for a lack of accurate congestion feedback. In this paper, we analyze the deficiencies of utilizing RTT as congestion signals and the applicability of learning algorithms in DCNs. Then, we propose DECC, a smart TCP congestion control algorithm for DCNs, which combines Deep Reinforcement Learning (DRL) with ECN to achieve high bandwidth utilization as well as low queuing delay. DECC fully utilizes precise in-network feedback and formulates several QoS requirements to a multi-objective function. Meanwhile, it decouples cwnd adjustment with DRL decision making to gradually learn the optimal congestion control policy in real-time. We evaluate the performance of DECC in various scenarios. Simulation results show that DECC can reduce the queue length at bottleneck switches by more than 50% compared to DCTCP, while maintaining high bandwidth utilization and reducing Flow Completion Time (FCTs) under burst traffic.

The upgrade of network devices to be equipped with multiple network interfaces makes it possible to improve network throughput performance through multipath transmission protocols, especially multipath TCP (MPTCP). However, so far the mostly used MPTCP protocols have a common limitation, namely the rigid and conservative method. They have been designed with little consideration of the fact that real networks are dynamic and the network status changes frequently, thus leading to the poor performance of current MPTCP in many realistic scenarios. In this paper, we propose a lightweight multipath congestion control algorithm based on online learning, named MP-OL. MP-OL models congestion control as a multi-armed bandit problem, and adjusts the sending rate of each subflow flexibly and adaptively through online learning. Therefore, MP-OL possesses the capability of suiting various network scenarios, and can achieve fairness and high performance in dynamic network environment. It can also flexibly switch between online learning and traditional method, which reduces the computational complexity while ensuring the learning efficiency, thus making MP-OL easy to deploy and use. As the experimental results demonstrated, compared with the leading MPTCP variants, MP-OL achieves significant improvements in fairness and link utilization, and shows better resilience to non-congestion loss and better adaptability to unstable network conditions. In real networks, MP-OL also obtains better throughput performance.

Multipath TCP (MPTCP) improves the bandwidth utilization in wireless network scenarios, since it can simultaneously utilize multiple interfaces for data transmission. However, with the fast growth of mobile devices and applications, link interruptions caused by handoffs still lead to drastic performance degradation in such scenarios. Typically, a series of packet losses on part of the links will block the transmission of the entire connection when handoff occurs. This paper proposes an Experience-driven Adaptive Redundant packet scheduler (EdAR) for MPTCP, aiming at achieving seamless handoffs in mobile networks. EdAR enables flexibly scheduling redundant packets with an experience-driven learning-based approach in the face of drastic network environment changes for multipath performance enhancement. To enable accurate learning and prediction, both the network environment and the best course of actions are jointly learned via a Deep Reinforcement Learning (DRL) agent, which we design with a hybrid structure to deal with the complexity of system states. Furthermore, both offline and online learning are utilized to allow the agent to adapt to different and changing network environments. Evaluation results show that EdAR outperforms the state-of-the-art MPTCP schedulers in most network scenarios. Specifically in mobile networks with frequent handoffs, EdAR brings$2\times $improvement in terms of the overall goodput.

A QUIC (Quick UDP Internet Connections) protocol is designed to improve Hypertext Transfer Protocol (HTTP) traffic and carries a non-negligible portion of the traffic in the current Internet. As its extension, Multipath QUIC (MPQUIC) provides higher bandwidth and smoother network handover by using multiple network interfaces simultaneously. However, to improve HTTP traffic, there are still some issues not yet carefully addressed in the existing MPQUIC, and packet scheduling is a vital one among the issues. Specifically, existing methods fail to respond to the stream prioritization of HTTP Version 2 (HTTP/2), leading to unsatisfying web page load performance. Besides, managing asymmetric and dynamic network paths is also a challenging issue, which may result in Head-of-Line (HoL) blocking and excessive buffer usage if not effectively handled. In this paper, we present a stream-aware per-packet scheduler, HoL Blocking Eliminating Scheduler (HBES), to improve the performance of MPQUIC in mobile networks. Firstly, HBES provides a fair allocation of aggregated bandwidth for different streams based on their priority. Then, it keeps stream data arriving at the receiver in order by estimating packet arrival time to mitigate HoL blocking and excessive buffer usage. We implement HBES and evaluate its performance in various network scenarios. Experimental results verify the superiority of HBES in reducing stream completion time and buffer occupation over those existing MPQUIC schedulers.

Bluetooth is a short-range wireless communication technology widely used by billions of personal computing, IoT, peripheral, and wearable devices. Bluetooth devices exchange commands and data, such as keyboard/mouse inputs, audio, and files, through a secure communication channel that is established through a pairing process. Due to the sensitivity of those commands and data, security mechanisms, such as encryption, authentication, and authorization, have been developed and adopted in the standards. Nevertheless, vulnerabilities continue to be discovered.

Image-to-video person re-identification aims to retrieve the same pedestrian as the image-based query from a video-based gallery set. Existing methods treat it as a cross-modality retrieval task and learn the common latent embeddings from image and video modalities, which are both less effective and efficient due to large modality gap and redundant feature learning by utilizing all video frames. In this work, we first regard this task as point-to-set matching problem identical to human decision process, and propose a novel Temporal Complementarity-Guided Reinforcement Learning (TCRL) approach for image-to-video person re-identification. TCRL employs deep reinforcement learning to make sequential judgments on dynamically selecting suitable amount of frames from gallery videos, and accumulate adequate temporal complementary information among these frames by the guidance of the query image, towards balancing efficiency and accuracy. Specifically, TCRL formulates point-to-set matching procedure as Markov decision process, where a sequential judgement agent measures the uncertainty between the query image and all historical frames at each time step, and verifies that sufficient complementary clues are accumulated for judgment (same or different) or one more frames are requested to assist judgment. Moreover, TCRL maintains a sequential feature extraction module with complementary residual detectors to dynamically suppress redundant salient regions and thoroughly mine diverse complementary clues among these selected frames for enhancing frame-level representation. Extensive experiments demonstrate the superiority of our method.

Shadow removal, which aims to restore the background in the shadow regions, is challenging due to its highly ill-posed nature. Most existing deep learning-based methods individually remove the shadow by only considering the content of the matched paired images, barely taking into account the auxiliary supervision of shadow generation in the shadow removal procedure. In this work, we argue that shadow removal and generation are interrelated and could provide useful informative supervision for each other. Specifically, we propose a new Bijective Mapping Network (BMNet), which couples the learning procedures of shadow removal and shadow generation in a unified parameter-shared framework. With consistent two way constraints and synchronous optimization of the two procedures, BMNet could effectively recover the underlying background contents during the forward shadow removal procedure. In addition, through statistical analysis of real world datasets, we observe and verify that shadow appearances under different color spectrums are inconsistent. This motivates us to design a Shadow-Invariant Color Guidance Module (SICGM), which can explicitly utilize the learned shadow-invariant color information to guide network color restoration, thereby further reducing color-bias effects. Experiments on the representative ISTD, ISTD+ and SRD benchmarks show that our proposed network outperforms the state-of-the-art method [11] in de-shadowing performance, while only using its 0.25% network parameters and 6.25% floating point operations (FLOPs).

Human gesture recognition using millimeter wave (mmWave) signals provides attractive applications including smart home and in-car interfaces. While existing works achieve promising performance under controlled settings, practical applications are still limited due to the need for intensive data collection, extra training efforts when adapting to new domains (i.e. environments, persons and locations) and poor performance for real-time recognition. In this paper, we propose DI-Gesture, a domain-independent and real-time mmWave gesture recognition system. Specifically, we first derive the signal variation corresponding to human gestures with spatial-temporal processing. To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework based on the correlation between signal patterns and gesture variations. Furthermore, we propose a dynamic window mechanism to perform gesture segmentation automatically and accurately, thus enabling real-time recognition. Finally, we build a lightweight neural network to extract spatial-temporal information from the data for gesture classification. Extensive experimental results show DI-Gesture achieves an average accuracy of 97.92%, 99.18% and 98.76% for new users, environments and locations, respectively. In real-time scenario, the accuracy of DI-Gesture reaches over 97% with an average inference time of 2.87ms, which demonstrates the superior robustness and effectiveness of our system.

As a promising complement to terrestrial cellular networks, such as 5G/6G, satellite networks have recently drawn increasing attention. However, facing the challenges of the rapidly increasing users' demand for multimedia content, how to achieve efficient data delivery in a dynamic environment becomes a critical but knotty problem. To provide an efficient solution from the routing perspective, in this paper, we consider the Information-Centric Networking (ICN) architecture and propose a content-aware routing scheme. The basic idea of the proposed routing scheme is to leverage the cached content on cache-enabled satellites and find the optimal route solution with maximum net-gains, i.e., how much delay is reduced. Considering the limitation of periodical signaling collection in satellite networks, we also design a cached content prediction model, which can infer the probability that a certain content could be cached according to the content's historical popularity information, to provide necessary information to measure net-gains. Extensive simulation results show that the proposed content-aware routing scheme outperforms the traditional routing scheme with a 20% reduction in terms of content retrieval delay and traffic consumption.

Software-Defined Networking (SDN) is a new and highly flexible network architecture, but the bottleneck between the control plane and the data plane makes it vulnerable to the control plane saturation DoS attacks. When the attack happens, traditional schemes in DoS scrubbing agent use a binary classification and a First In First Out (FIFO) queue to filter attack flows. However, this scheme is inimical to the end-to-end latency of benign traffic. To tackle this issue, we propose LLDM, leveraging a dynamic priority scheme and a priority queue to detect, mitigate the attacks while ensuring low latency for benign traffic. After detecting the attack, LLDM leverages a two-phase scheme for mitigation. First, LLDM marks packets from the ports under attack as suspicious and migrates them to the mitigation agent. Then, the dynamic priority manager assigns each packet a priority corresponding to its legality, which is used in the priority queue for DoS scrubbing. We evaluate LLDM in a simulation SDN environment. The experimental results show that LLDM can reduce 90.4% of the queuing delay compared with the traditional scheme under a 5000 Packets Per Second (PPS) attack, and it is also resistant to more sophisticated attacks. Under the high rate attack of 50000 PPS, LLDM installs a flow rule for legitimate traffic in 0.2 seconds. Moreover, for benign HTTP requests, LLDM can keep the request time at 1.39 seconds.

In Software-Defined Networking (SDN), the controllers implement flexible and scalability networking policies by installing different flow rules. Each rule matches a specific class of flows, instructs the switches to execute actions, and then expires when they finish their tasks. OpenFlow introduces the timeout mechanism to manage these flow rules. However, finding a reasonable timeout value becomes a difficult problem for the network managers. When a relatively small timeout value is given to an elephant flow, the rule expires early, introducing extra cost for the controller and long latency for the matching flow, respectively. On the contrary, a large timeout value for a mice flow makes a rule occupy the switch memory too long, wasting the caching memory and causing the flow table prone to overflow. Therefore, it is necessary to allocate appropriate timeouts for different flows dynamically. In this paper, we achieve this goal with real-time traffic monitoring and heuristic algorithms. By considering different network loads and designing corresponding dynamic timeout algorithms for different scenarios, we make full use of the advantages of SDN to improve the utilization rate of the switch memory and save the controller resources. Further, we implement our scheme in a simulation SDN platform and evaluate the algorithms with the public datasets. Experiments show that our scheme has low control overhead and is memory efficient compared with current mechanisms.

Fall is a severe health threat for elders’ health care. While existing systems could achieve promising performance under specific scenarios, the required computing resources are usually not affordable, which is not applicable for real-time detection. In this paper, we propose mmFall, a real time fall detection system using millimeter wave signal which can achieve impressive accuracy with low computation complexity. Specifically, we first extract the signal variation corresponding to human activity with spatial-temporal processing. To enhance the system performance and robustness, we perform data augmentation by shifting, flipping, extracting and interpolating the signal. Finally, we design a light-weight convolutional neural network to achieve real-time fall detection. Extensive experimental results demonstrate that the pro-posed system could achieve state-of-the-art performance with limited computation complexity.

Human can extrapolate well, generalize daily knowledge into unseen scenarios, raise and answer counterfactual questions. To imitate this ability via generative models, previous works have extensively studied explicitly encoding Structural Causal Models (SCMs) into architectures of generator networks. This methodology, however, limits the flexibility of the generator as they must be carefully crafted to follow the causal graph, and demands a ground truth SCM with strong ignorability assumption as prior, which is a nontrivial assumption in many real scenarios. Thus, many current causal GAN methods fail to generate high fidelity counterfactual results as they cannot easily leverage state-of-the-art generative models. In this paper, we propose to study counterfactual synthesis from a new perspective of knowledge extrapolation, where a given knowledge dimension of the data distribution is extrapolated, but the remaining knowledge is kept indistinguishable from the original distribution. We show that an adversarial game with a closed-form discriminator can be used to address the knowledge extrapolation problem, and a novel principal knowledge descent method can efficiently estimate the extrapolated distribution through the adversarial game. Our method enjoys both elegant theoretical guarantees and superior performance in many scenarios.

In this paper, we present a novel shadow detection framework by investigating the mutual complementary mechanisms contained in this specific task. Our method is based on a key observation: in a single shadow image, shadow regions and non-shadow counterparts are complementary to each other in nature, thus a better estimation on one side leads to an improved estimation on the other, and vice versa. Motivated by this observation, we first leverage two parallel interactive branches to jointly produce shadow and non-shadow masks. The interaction between two parallel branches is to retain the deactivated intermediate features of one branch by introducing the negative activation technique, which could serve as complementary features to the other branch. Besides, we also apply identity reconstruction loss as complementary training guidance at the image level. Finally, we design two discriminative losses to satisfy the complementary requirements of shadow detection, i.e., neither missing any shadow regions nor falsely detecting non-shadow regions. By fully exploring and exploiting the complementary mechanism of shadow detection, our method can confidently predict more accurate shadow detection results. Extensive experiments on the three widely-used benchmarks demonstrate our proposed method achieves superior shadow detection performance against state-of-the-art methods with a relatively low computational cost.

Compared with optical imaging, radio frequency (RF) imaging enables object imaging in an all-weather, privacy-preserving, and cost-effective way. However, the existing heuristic solutions such as back projection (BP) and range migration (RM), suffer from the sidelobe interference due to limited antenna aperture. In this paper, we propose a super-resolution 3-D imaging algorithm to enhance imaging performance. Mathematically, the reconstruction problem is an inverse problem that can be formulated as an optimization problem. The low-rank property of the object is exploited to regularize the imaging problem by nuclear norm minimization. We evaluate the proposed algorithm with both simulations and measurements. With a frequency band range from 2.7-4.1 GHz and a 16×6 multiple-input-multiple-output (MIMO) antenna array, simulation results show high imaging quality with a median boundary keypoint precision of 2 cm, and experimental results validate the feasibility of the proposed algorithm in a real-world environment.

Radio-frequency (RF) based human sensing technologies, due to their great practical value in various applications and privacy-preserving nature, have gained tremendous attention in recent years. However, without fully exploiting the characteristics of radio signals, the performance of existing methods are still limited. First, RF features of the moving human body have different representations in dimensions such as channel and scale, which is challenging when performing feature fusion. Besides, the human body is specularly reflective with respect to the radar, which means the human body cannot be fully captured by a single RF snapshot. Therefore, the radar signal reflected by the human body is sparse and incomplete, which is difficult to extract high-quality features for 3D human pose estimation. In this paper, we present the RF-based Pose Machines (RPM), a novel framework which can generate 3D skeletons from RF signals. Considering the characteristics of RF signals, RPM includes several modules to overcome the challenges. Firstly, a Multidimensional Feature Fusion (MFF) backbone is designed to effectively fuse radio signals based on the channels' correlation and maintain high-quality feature via a multi-scale fusion block. A Spatio-Temporal Attention network is then designed to reconstruct 3D skeletons by modeling the non-local spatio-temporal relationships. To evaluate the performance of our RPM framework, we construct a large-scale dataset of synchronized 3D skeletons and RF signals, RFSkeleton3D. Our experimental results show that RPM locates 3D key points of the human body with an average error of 5.71cm and maintains its performance in new environments with occlusion or bad illumination. The dataset and codes will be made in public.

This paper tackles a new challenge: how to generate human pose images from wireless signals? Although the optical camera can capture optical images, it is easily restricted by bad lighting. The wireless signals do not rely on visible lights. However, the low-resolution characteristics make previous works can only generate a rough skeleton of human posture, missing a lot of detailed visual information, such as background, appearance, etc. Since the visual information usually maintains unchanged for a period and the wireless signals can capture the movements of the human, in this paper, we propose a framework to generate the target human pose images by combining the wireless signals with an initial optical image. We utilize multiple wireless devices to collect the WiFi signals and a camera to capture the initial optical image. Then a data preprocessing component is designed to preprocess the wireless and vision data. Finally, a deep learning model learns to generate the human pose images from the processed wireless signals and the initial optical image. We conduct experiments to evaluate our proposed framework and results show that it achieves higher accuracy than the state-of-the-art WiFi-based pose estimation method and better visual quality than the state-of-the-art human generation method.

By leveraging the mechanical motions on the body surface conducted by physiological activities, many works have achieved radio-frequency(RF)-based physiological sensing. However, previous works generally simplify the model on both the mechanism of physiological motion and the signal propagation around the human body, which leads to the loss of valuable information. In this paper, we introduce the concept of micro-motion(MM) camera to provide a more cognitive imaging modality to observe torso surface motion comprehensively so as to dynamically image the motions of the breath and cardiac activities. We develop a sub-6G MMCamera prototype system. The camera functionality is implemented to prove the concept novelty from the view of respiratory-cardiovascular system monitoring. Our result shows that the proposed system could provide fine-grid torso surface motion imaging with breath and cardiac activities distributed over the entire thorax and abdomen.

Vibration is a widespread physical phenomenon that often carries important information such as the internal state of the devices. Thus, vibration measurement is of great importance in the field of modern engineering and has drawn much attention. While achieving promising performance, existing methods fail when the vibration amplitude is tiny, e.g, smaller than 50 um. To address such a challenge, in this paper, we propose a contactless method with deep learning, denoted as DeepVib, to sense the tiny vibration using millimeter wave radar. Specifically, DeepVib first makes full advantage of the physical characteristics of the vibrating object and combines Range-Doppler FFT to find the range bin of vibrating objects. Then, DeepVib trains a denoising neural network using a large amount of simulated data, which takes the noisy sample points as input and outputs the denoised data with better SNR. Finally, the vibration status is recovered through the phase variation of the extracted signal. Simulation results show that DeepVib achieves over 40% improvement in measuring um-level amplitudes with over 5x faster processing time, while real experimental results show that DeepVib achieves a mean amplitude error of 2.1 um for the 100um-amplitude vibration.

Existing tools for the automated detection of memory corruption bugs are not very effective in practice. They typically recognize only standard memory management (MM) APIs (e.g., malloc and free) and assume a naive paired-use model—an allocator is followed by a specific deallocator. However, we observe that programmers very often design their own MM functions and that these functions often manifest two major characteristics: (1) Custom allocator functions perform multi-object or nested allocation which then requires structure-aware deallocation functions. (2) Custom allocators and deallocators follow an unpaired-use model. A more effective detection thus needs to adapt those characteristics and capture memory bugs related to non-standard MM behaviors. In this paper, we present a MM function aware memory bug detection technique by introducing the concept of structure-aware and object-centric Memory Operation Synopsis (MOS). A MOS abstractly describes the memory objects of a given MM function, how they are managed by the function, and their structural relations. By utilizing MOS, a bug detection could explore much less code but is still capable of handling multi-object or nested allocations and does not rely on the paired-use model. In addition, to extensively find MM functions and automatically generate MOS for them, we propose a new identification approach that combines natural language processing (NLP) and data flow analysis, which enables the efficient and comprehensive identification of MM functions, even in very large code bases. We implement a MOS-enhanced memory bug detection system, Goshawk, to discover memory bugs caused by complex and custom MM behaviors. We applied Goshawk to well-tested and widely-used open source projects including OS kernels, server applications, and IoT SDKs. Goshawk outperforms the state-of-the-art data flow analysis driven bug detection tools by an order of magnitude in analysis speed and the number of accurately identified MM functions, reports the discovered bugs with a developer-friendly, MOS based description, and successfully detects 92 new double-free and use-after-free bugs.

Remote entanglement distribution plays a crucial role in large-scale quantum networks, and the key enabler for entanglement distribution is quantum routers (or repeaters) that can extend the entanglement transmission distance. However, the performance of quantum routers is far from perfect yet. Amongst the causes, the limited quantum memories in quantum routers largely affect the rate and efficiency of entanglement distribution. To overcome this challenge, this paper presents a new modeling for the maximization of entanglement distribution rate (EDR) on a memory-limited path, which is then transformed into entanglement generation and swapping sub-problems. We propose a greedy algorithm for short-distance entanglement generation so that the quantum memories can be efficiently used. As for the entanglement swapping sub-problem, we model it using an Entanglement Graph (EG), whose solution is yet found to be at least NP-complete. In light of it, we propose a heuristic algorithm by dividing the original EG into several sub-problems, each of which can be solved using dynamic programming (DP) in polynomial time. By conducting simulations, the results show that our proposed scheme can achieve a high EDR, and the developed algorithm has a polynomial-time upper bound and reasonable average runtime complexity.

Entanglement routing establishes remote entanglement connection between two arbitrary nodes, which is one of the most important functions in quantum networks. The existing routing mechanisms mainly improve the robustness and throughput facing the failure of entanglement generations, which, however, rarely include the considerations on the most important metric to evaluate the quality of connection, entanglement fidelity. To solve this problem, we propose purification-enabled entanglement routing designs to provide fidelity guarantee for multiple Source-Destination (S-D) pairs in quantum networks. In our proposal, we first consider the single S-D pair scenario and design an iterative routing algorithm, Q-PATH, to find the optimal purification decisions along the routing path with minimum entangled pair cost. Further, a low-complexity routing algorithm using an extended Dijkstra algorithm, Q-LEAP, is designed to reduce the computational complexity by using a simple but effective purification decision method. Finally, we consider the common scenario with multiple S-D pairs and design a greedy-based algorithm considering resource allocation and re-routing process for multiple routing requests. Simulation results show that the proposed algorithms not only can provide fidelity-guaranteed routing solutions, but also has superior performance in terms of throughput, fidelity of end-to-end entanglement connection, and resource utilization ratio, compared with the existing routing scheme.

As a new architecture of Internet infrastructure, Information-Centric Networking (ICN) is mainly designed to effectively handle the rapidly increasing user demand for content delivery through in-network caching. While facilitating the dissemination of content to users and making better use of the network resources, ICN is also vulnerable in that attackers can inject poisoned content into the network and isolate users from valid content sources. The introduction of signature verification in each router can effectively prevent this attack, but it also introduces great computation overhead. Existing schemes in ICN reduce verification overhead from a single routing perspective but do not consider integrating resources within ICN for collaborative content authentication and cyber self-defense. In this paper, we propose a collaborative, secure, and efficient content validation protection framework, named CSEVP, to implement a multi-router collaborative defense mechanism for ICN. On the one hand, we conduct content verification by probabilistically choosing one router involved in the transmission path to offload the computation overhead of content verification from a single router to multiple ones. On the other hand, we adopt bloom filters for routers to record and share verification results to further facilitate a more efficient content validity verification. The security and efficiency analysis shows that our proposed CSEVP can achieve efficient content validity verification among multiple routers with acceptable low communication and storage overhead.

As a promising implementation of Information-Centric Networking (ICN), Named Data Networking (NDN) has potential advantages over the TCP/IP network in content distribution, mobility support, etc. However, the research on NDN is still in its infancy, and congestion control, NDN’s most important functional element, poses many challenges, such as congestion detection, excessive window reduction for non-congested paths, and unfairness. In this paper, we propose an Intelligent Edge-Aided Congestion Control (IEACC) scheme for the NDN network based on Deep Reinforcement Learning (DRL). The proposed IEACC provides a proactive congestion detector that utilizes intermediate routers to transmit accurate congestion information along the path to consumers through data packets. Furthermore, considering the multi-source transmission in NDN, IEACC divides data packets into different congestion degrees by a lightweight clustering algorithm and provides suitable inputs for DRL, thereby obtaining a reasonable transmission rate. Then, it distributes the estimated bandwidth resources to consumers with transmission needs to maintain fairness. Finally, we implement our proposed scheme in the simulation platform and evaluate the performance in different scenarios. The results show that it can improve data transmission rate, reduce packet loss, and maintain fairness compared with others.

Video-based person re-identification aims to match pedestrians from video sequences across non-overlapping camera views. The key factor for video person re-identification is to effectively exploit both spatial and temporal clues from video sequences. In this work, we propose a novel Spatial-Temporal Correlation and Topology Learning framework (CTL) to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. Specifically, CTL utilizes a CNN backbone and a key-points estimator to extract semantic local features from human body at multiple granularities as graph nodes. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body. Moreover, a 3D graph convolution and a cross-scale graph convolution are designed, which facilitate direct cross-spacetime and cross-scale information propagation for capturing hierarchical spatial-temporal dependencies and structural information. By jointly performing the two convolutions, CTL effectively mines comprehensive clues that are complementary with appearance information to enhance representational capacity. Extensive experiments on two video benchmarks have demonstrated the effectiveness of the proposed method and the state-of-the-art performance.

Digital image manipulations have been heavily abused to spread misinformation. Despite the great efforts dedicated in research community, prior works are mostly performance-driven, i.e., optimizing performances using standard/heavy networks designed for semantic classification. A thorough understanding for fake images detection models is still missing. This paper studies the essential ingredients for a good fake image detection model, by profiling the best-performing architectures. Specifically, we conduct a thorough analysis on a massive number of detection models, and observe how the performances are affected by different patterns of network structure. Our key findings include: 1) with the same computational budget, flat network structures (e.g., large kernel sizes, wide connections) perform better than commonly used deep networks; 2) operations in shallow layers deserve more computational capacities to trade-off performance and computational cost. These findings sketch a general profile for essential models of fake image detection, which show clear differences with those for semantic classification. Furthermore, based on our analysis, we propose a new Depth-Separable Search Space (DSS) for fake image detection. Compared to state-of-the-art methods, our model achieves competitive performance while saving more than 50% parameters.

Service prioritization brings reasonable allocation of network resources and improves the overall quality of experience (QoE) of users, but it has not been thoroughly investigated in information centric networking (ICN). Existing works lack adaptability and they cannot ensure specific content provider (CP) get well caching service which is one of the most important functions in ICN. In this paper, we firstly propose a service prioritization scheme to flexibly provide different caching services for heterogeneous CPs to improve the overall network efficiency. The main idea is to allocate dedicated cache space for paying CPs and provide prioritized caching service for them, while normal CPs only enjoy the normal caching service. The scheme can be divided into two phases. First, we select a group of nodes with higher importance as core nodes based on network topology, and pair each edge node to a core node following the two-sided many-to-one matching algorithm. Second, we dynamically allocate and manage the dedicated cache space for core nodes. We model the allocation of dedicated cache space and convert it into a convex optimization problem to solve. After that, a practical caching strategy and system design are implemented in the ndnSIM simulator. Finally, we evaluate our scheme and conduct comparative experiments with the most representative work diff-caching, simulation results show that our scheme outperform it in terms of both delay and cache hit ratio.

The whole Software-Defined Networking (SDN) system might be out of service when the control plane is overloaded by control plane saturation attacks. In this attack, a malicious host can manipulate massive table-miss packets to exhaust the control plane resources. Even though many studies have focused on this problem, systems still suffer from more influenced switches because of centralized mitigation policies, and long recovery delay because of the remaining attack flows. To solve these problems, we propose FSDM, a Fast recovery Saturation attack Detection and Mitigation framework. For detection, FSDM extracts the distribution of Control Channel Occupation Rate (CCOR) to detect the attack and locates the port that attackers come from. For mitigation, with the attacker's location and distributed Mitigation Agents, FSDM adopts different policies to migrate or block attack flows, which influences fewer switches and protects the control plane from resource exhaustion. Besides, to reduce the system recovery delay, FSDM equips a novel functional module called Force_Checking, which enables the whole system to quickly clean up the remaining attack flows and recovery faster. Finally, we conducted extensive experiments, which show that, with the increasing of attack PPS (Packets Per Second), FSDM only suffers a minor recovery delay increase. Compared with traditional methods without cleaning up remaining flows, FSDM saves more than 81% of ping RTT under attack rate ranged from 1000 to 4000 PPS, and successfully reduced the delay of 87% of HTTP requests time under large attack rate ranged from 5000 to 30000 PPS.

This paper presents the generalized butterfly graph (GBG) and its application to video stream authentication. Compared with the original butterfly graph, the proposed GBG provides significantly increased flexibility, which is necessary for streaming applications, including supporting arbitrary bit-rate budget for authentication and arbitrary number of video packets. Within the GBG design, the problem of constructing an authentication graph is defined as follows: given the total number of packets to protect, the expected packet loss rate for the network, and the available overhead budget, how should one design the authentication graph to maximize the probability that the received packets are verifiable? Furthermore, given the fact that media packets are typically of unequal importance, we explore two variants of the GBG authentication, packet sorting and unequal authentication protection, which apply unequal treatment to different packets based on their importance. Lastly, we examine how the proposed GBG authentication can be applied within the context of rate-distortion-authentication (R-D-A) optimized streaming: given a media stream protected by GBG authentication, the R-D-A optimized streaming technique computes an optimized transmission schedule by recognizing and accounting for the authentication dependencies in the GBG authentication graph.

Bit extraction is an essential component of an image hashing system. A good bit extraction scheme should preserve the performance achieved at the feature representation level. In other words the robustness discrimination tradeoff measured by ROC analysis should be preserved. This is dependent on several factors such as, the encountered noise and the number of bits that can be extracted per sample. This paper investigates the relationship between these parameters and proposes some theoretical bounds in achieving a good tradeoff. The analysis primarily focuses on the bit extraction method proposed in [1] and its performance is compared with a scalar quantization based hashing method.

Detection of planar surfaces in a generic scene is difficult when the illumination is complex and less intense, and the surfaces have non-uniform colors (e.g., a movie poster). As a result, the specularity, if appears, is superimposed with the surface color pattern, and hence the observation of uniform specularity is no longer sufficient for identifying planar surfaces in a generic scene as it does under a distant point light source. In this paper, we address the problem of planar surface recognition in a single generic-scene image. In particular, we study the problem of recaptured photo recognition as an application in image forensics. We discover that the specularity of a recaptured photo is modulated by themesostructure of the photo surface, and its spatial distribution can be used for differentiating recaptured photos from the original photos. We validate our findings in real images of generic scenes. Experimental results show that there is a distinguishable feature of natural scene and recaptured images. Given the definition of specular ratio as the percentage of specularity in the overall measured intensity, the distribution of specular ratio image’s gradient of natural images is Laplacian-like while that of recaptured images is Rayleigh-like.

This paper shows how Rate-Distortion-Authentication (R-D-A) optimized streaming may be performed with the Generalized Butterfly Graph (GBG) stream authentication method. R-D-A streaming is designed to compute an optimized transmission policy by accounting for both coding and authentication dependencies, and GBG is designed to protect the authenticity of a media stream. The GBG graph is better suited for streaming than the original Butterfly graph since it is highly flexible and can support an arbitrary number of packets and arbitrary overhead. However, the dependency chains within the GBG graph are much longer and are tangled with each other — making it very difficult to quantify the dependencies necessary to perform R-D-A streaming. We propose a method to estimate the authentication dependencies, which is then used by the R-D-A technique to compute the optimized transmission policy.

This paper presents a novel content-aware joint media error and authentication protection scheme based on error correcting coding (ECC). The innovation of the proposed scheme lies in the true joint design of error protection and authentication verification. This is fundamentally different from many existing schemes in which they consider media authentication separately from other media processing components such as error protection in media communication systems. By making use of the channel information and integrating the authentication with error protection necessary in contemporary media communication systems, we are able to achieve 100% complete verification with low authentication overhead. With such integration, the end-to-end media quality and media security guarantee can be obtained. Based on this joint error and authentication protection framework, an optimal rate allocation algorithm under certain source and channel models is also developed. Simulations based on JPEG 2000 images have been carried out to validate the proposed scheme and the simulation results show that the proposed approach is indeed able to achieve simultaneous error and authentication protection for JPEG 2000 images.

In this paper, a novel method of multi-strategy object tracking for video surveillance is proposed. Under this framework, the moving and stationary objects are tracked separately, so that different reliable features for different types of objects can be exploited efficiently. For a moving object, the global color features called Dominant Color Histogram (DCH) are reliable for object tracking. An efficient sequential approach is employed, which first estimates the depth order of the objects using DCH, then tracks each individual one-by-one with mean-shift and exclusion operations. For a stationary object, the image template is accurate for object representation and matching. A layer model is employed for stationary object tracking. Stationary objects are classified as “visible”, “occluded”, and “removed”. For people stop moving in scene, they seldom stay completely motionless. Therefore, two more states, “changing pose”, and “start moving” are added. Once the stationary person is detected as “start moving”, he will be switched to moving object tracking seamlessly. The proposed method has been successfully applied in a real-time intelligent CCTV surveillance system for unusual event detection and tested in both real-world public sites and public datasets from PETS2006. Very encouraging results have been obtained.

Graphical password (i.e., image based authentication) is considered as a promising alternative to traditional textual password for mobile devices, to achieve better tradeoff between usability and security. However, previous proposals of graphical password have the limitation of limited entropy. In this paper, we propose a new scheme incorporating user face based authentication into the association-based graphical password solution we proposed before, aiming at achieving higher security without compromising user-friendliness for mobile application scenarios. System performance analysis and comparisons with other schemes are presented to validate our scheme.

The need for security services, such as confidentiality and authentication, has become one of the major concerns in multimedia communication applications, such as video on demand and peer-to-peer content delivery. Conventional data authentication cannot be directly applied for streaming media when an unreliable channel is used and packet loss may occur. This paper begins by reviewing existing end-to-end media authentication schemes, which can be classified into stream-based and content-based techniques. We then motivate and describe how to design authentication schemes for multimedia delivery that exploit the unequal importance of different packets. By applying conventional cryptographic hashes and digital signatures to the media packets, the system security is similar to that achievable in conventional data security. However, instead of optimizing packet verification probability, we optimize the quality of the authenticated media, which is determined by the packets that are received and able to be decoded and authenticated. The quality of the authenticated media is optimized by allocating the authentication resources unequally across streamed packets based on their relative importance, thereby providing unequal authenticity protection. The effectiveness of this approach is demonstrated through experimental results on different media types (image and video), different compression standards (JPEG, JPEG2000, and H.264), and different channels (wired with packet erasures and wireless with bit errors).

Recently, among various data hiding techniques, a new subset, lossless data hiding, has received increasing interest. Most of the existing lossless data hiding algorithms are, however, fragile in the sense that the hidden data cannot be extracted out correctly after compression or other incidental alteration has been applied to the stego-image. The only existing semi-fragile (referred to as robust in this paper) lossless data hiding technique, which is robust against high-quality JPEG compression, is based on modulo-256 addition to achieve losslessness. In this paper, we first point out that this technique has suffered from the annoying salt-and-pepper noise caused by using modulo-256 addition to prevent overflow/underflow. We then propose a novel robust lossless data hiding technique, which does not generate salt-and-pepper noise. By identifying a robust statistical quantity based on the patchwork theory and employing it to embed data, differentiating the bit-embedding process based on the pixel group's distribution characteristics, and using error correction codes and permutation scheme, this technique has achieved both losslessness and robustness. It has been successfully applied to many images, thus demonstrating its generality. The experimental results show that the high visual quality of stego-images, the data embedding capacity, and the robustness of the proposed lossless data hiding scheme against compression are acceptable for many applications, including semi-fragile image authentication. Specifically, it has been successfully applied to authenticate losslessly compressed JPEG2000 images, followed by possible transcoding. It is expected that this new robust lossless data hiding algorithm can be readily applied in the medical field, law enforcement, remote sensing and other areas, where the recovery of original images is desired.

In the paper we study the efficiency of semantic concept association in multimedia semantic concept detection. We present an approach to automatically learn from the corpus the association strength between pair-wise semantic concepts. We discuss two usages of association strength: 1) applying positive concepts with high association strength for selecting expressive component in the model-based fusion and 2) applying negative concepts with low association strength as filters. We evaluate its efficiency on the task of semantic concept detection on the large-scale news video dataset from TRECVID 2005 development set. Our experimental results demonstrate that exploiting positive association reduces the size of feature dimension in the model-based fusion and significantly improves the rank performance of system. The mean average precision is increased to 0.215 on the validation set and 0.206 on the evaluation set. Compared to the traditional model-based fusion, the improvement is about 9.1% and 3.5%, respectively. The average feature dimension is reduced to 43 from 312.

Video streaming with authentication is practically important, where a packet is decoded only when it is both received and authenticated. Recent work examined the problem of rate-distortion-authentication (R-D-A) optimized streaming of authenticated video. The original R-D-A technique assumes that each packet has only one deadline, its display deadline, and that a packet is not considered for transmission after its deadline. However, for video protected with an inter-packet graph-based authentication technique, a video packet can still be useful for verification of other packets even if it misses its own display deadline. We formulate the problem of multiple-deadline R-D-A optimized streaming and also propose ways to reduce the complexity. Simulation results using H.264 and NS-2 demonstrate that multiple-deadline R-D-A optimization achieves performance improvements of up to 4 dB over single-deadline R-D-A optimization.

The bag-of-words approach has become increasingly attractive in the fields of object category recognition and scene classification, witnessed by some successful applications [5, 7, 11]. Its basic idea is to quantize an image using visual terms and exploit the image-level statistics for classification. However, the previous work still lacks the capability of modeling the spatial dependency and the correspondence between patches and object parts. Moreover, quantization always deteriorates the descriptive power of the patch feature. This paper proposes the hidden maximum entropy (HME) approach for modeling the object category. Each object is modeled by the parts, each having a Gaussian distribution. The spatial dependency and image-level statistics of parts are modeled through the maximum entropy approach. The model is learned by an EM-IIS (expectation maximum embedded with improved iterative scaling) algorithm. Our experiments on the Caltech 101 dataset show that the relative reduction of equal error rate of 23.5 % and relative improvement of AUC (area under ROC) of 22.0 % are obtained when comparing the HME based system with the ME based baseline system.

An image hash should be (1) robust to allowable operations and (2) sensitive to illegal manipulations and distinct queries. Some applications also require the hash to be able to localize image tampering. This requires the hash to contain both robust content and alignment information to meet the above criterion. Fulfilling this is difficult because of two contradictory requirements. First, the hash should be small and second, to verify authenticity and then localize tampering, the amount of information in the hash about the original required would be large. Hence a tradeoff between these requirements needs to be found. This paper presents an image hashing method that addresses this concern, to not only detect but also localize tampering using a small signature (< 1kB). Illustrative experiments bring out the efficacy of the proposed method compared to existing methods.

This paper proposes a stream authentication method based on the generalized butterfly graph (GBG) framework. Compared with the original Butterfly graph, the proposed GBG graph supports an arbitrary overhead budget and number of packets. Within the GBG framework, the problem of constructing an authentication graph is considered as a design problem: Given total number of packets, packet loss rate, and overhead budget, we show how to design the graph (number of rows and columns and edge allocation among nodes) to maximize the expected number of verified packets. In addition, we also propose a new evaluation metric called loss-amplification-factor (LAF), which measures the extent to which the authentication method exacerbates the effective packet loss rate. Experimental results demonstrate significant performance improvements over existing authentication methods like EMSS, augmented chain, and the original Butterfly.

Recently, the bag-of-words approach has been successfully applied to automatic image annotation, object recognition, etc. The method needs to first quantize an image using the visual terms and then extract the image-level statistics for classification. Although successful applications have been reported, it lacks the capability to model the spatial dependency and the correspondence between the patches and visual parts. Moreover, quantization deteriorates the descriptive power of patch feature. This paper proposes the hidden maximum entropy (HME) approach for modeling visual concepts. Each concept is composed of a set of visual parts, each part having a Gaussian distribution. The spatial dependency and image-level statistics of parts are modeled through the maximum entropy. The model is learned using the developed EM-IIS algorithm. We report the preliminary results on the 260 concepts in the Corel dataset and compared with the maximum entropy (ME) approach. Our experiments on concept detection show that (1) a relative increment of 10.3% is observed when comparing the average AUC value of HME approach with that of the ME approach and (2) the HME approach reduces the average equal error rate from 0.412 for the ME approach to 0.354.

Content-dependent watermarking (CDWM) has been proposed as a solution to overcome the potential estimation attack aiming to recover and remove the watermark from the host signal. It has also been used for the application of content authentication. In this work, we first present an analysis on why some prior work on CDWM pose potential security problems due to their inherent cryptographic weakness. With the aim of achieving cryptographic level of security, we then propose a novel CDWM scheme based on homomorphic encryption and dirty paper precoding. The general idea is to introduce a decryption module before watermark detection to create some nonlinearity and thereby inhibit conventional watermark attacks based on linear operations. We conclude this paper by bringing up some thoughts on the integration of watermarking and cryptography.

Digital images can be forged easily with today's widely available image processing software. In this paper, we describe a passive approach to detect digital forgeries by checking inconsistencies of blocking artifact. Given a digital image, we find that the blocking artifacts introduced during JPEG compression could be used as a "natural authentication code". A blocking artifact measure is then proposed based on the estimated quantization table using the power spectrum of the DCT coefficient histogram. Experimental results also demonstrate the validity of the proposed approach.

In our previous work (Li et al., 2006), we have presented unequal authenticity protection (UAP), the methodology of effective protecting multimedia stream transmitted over error-prone wireless networks. In this paper, we extend the previous analysis and consider integrating UAP into the joint source-channel coding (JSCC) framework to achieve optimization of end-to-end quality of media content. We further illustrate the effectiveness of this system using an implementation on progressive JPEG coder.

Given the rich content-based features of multimedia (e.g., visual, text, or audio) and the development of various approaches to automatic detectors (e.g., SVM, Adaboost, HMM or GMM, etc), can we find an efficient approach to combine these evidences? In the paper, we address this issue by proposing an Integrated Statistical Model (ISM) to combine diverse evidences extracted from the domain knowledge of detectors, the intrinsic structure of modality distribution and inter-concept associations. The ISM provides a unified framework for evidence fusion, having the following unique advantages: 1) the intrinsic modes in the modality distribution are discovered and modeled by a generative model; 2) each mode is a partial description of structure of the modality and the mode configuration, i.e. a set of modes, and is a new representation of the document content; 3) mode discrimination is automatically learned; 4) prior knowledge such as detector correlations and inter-concept relations can be explicitly described and integrated. More importantly, an efficient pseudo-EM algorithm is realized for training the statistical model. The learning algorithm relaxes the computational cost due to the normalized factor and latent variables in the graphical model. We evaluate system performance of our multimedia semantic concept detection with the TRECVID 2005 development dataset, in terms of efficiency and capacity. Our experimental results demonstrate that the ISM fusion outperforms the SVM based discriminative fusion method.

In this paper, a new flexible layered authentication graph (FLAG) algorithm is proposed for multimedia streaming authentication. While maximizing the verification probability by avoiding authentication path overlapping, this algorithm allows flexible communication overhead in terms of the number of hash links, as well as flexible authentication group size. These flexibilities make FLAG an excellent candidate for multimedia streaming authentication, in that (i) in the sender buffering mode, it allows elastic sending delay required by multimedia streaming congestion control; (ii) in the receiver buffering mode, it facilitates adaptation to effective network bandwidth; (iii) it also has the potential to provide unequal authentication protection (UAP), which is a natural solution for multimedia code stream. Our analysis and experiment results further confirm the validity of our algorithm.

There are great challenges in streaming variable-bit-rate video over wide-area networks due to the significant variation of network conditions. The utilization of the precious bandwidth of wide-area networks is often low in such streaming systems. In this paper, we propose a novel framework to improve the bandwidth utilization from a new perspective. Instead of focusing on the performance of each single media stream, we aim to improve the overall bandwidth utilization for video streaming systems. We try to exploit the unoccupied bandwidth in ongoing streams and using it to deliver some prefetched data which can be used to facilitate future streaming. Preliminary results show that our mechanism has great potential to improve both the overall bandwidth utilization and the caching performance of the proxy servers in the streaming systems.

Efficient delivery of streaming media content over the Internet becomes an important area of research as such content is rapidly gaining its popularity. Many research works studied this problem based on the client-proxy-server structure and proposed various mechanisms to address this problem such as proxy caching and prefetching. While the existing techniques can improve the performance of accesses to reused media objects, they are not so effective in reducing the startup delay for first-time accessed objects. In this paper, we try to address this issue by proposing a more aggressive prefetching scheme to reduce the startup delay of first-time accesses. In our proposed scheme, proxy servers aggressively prefetch media objects before they are requested. We make use of servers' knowledge about access patterns to ensure the accuracy of prefetching, and we try to minimize the prefetched data size by prefetching only the initial segments of media objects. Results of trace-driven simulations show that our proposed prefetching scheme can effectively reduce the ratio of delayed requests by up to 38% with very marginal increase in traffic.

We define authenticated video as decoded video that results from those received packets whose authenticities have been verified. Generic data stream authentication methods usually impose overhead and dependency among packets for verification. Therefore, the conventional rate-distortion (R-D) optimized video streaming techniques produce highly sub-optimal R-D performance for authenticated video, since they do not account for the overhead and additional dependencies for authentication. In this paper, we study this practical problem and propose an Rate-Distortion-Authentication (R-D-A) optimized streaming technique for authenticated video. Based on packets' importance in terms of both video quality and authentication dependencies, the proposed technique computes a packet transmission schedule that minimizes the expected end-to-end distortion of the authenticated video at the receiver subject to a constraint on the average transmission rate. Simulation results based on H.264 JM 10.2 and NS-2 demonstrate that our proposed R-D-A optimized streaming technique substantially outperforms both prior (authentication-unaware) R-D optimized streaming techniques and data stream authentication techniques. In particular, when the channel capacity is below the source rate, the PSNR of authenticated video quickly drops to unacceptable levels using conventional R-D optimized streaming techniques, while the proposed R-D-A Optimization technique still maintains optimized video quality. Furthermore, we examine a low-complexity version of the proposed algorithm, and also an enhanced version which accounts for the multiple deadlines associated with each packet, which is introduced by stream authentication

In this paper, a kernel-based learning algorithm, kernel rank, is presented for improving the performance of semantic concept detection. By designing a classifier optimizing the receiver operating characteristic (ROC) curve using kernel rank, we provide a generic framework to optimize any differentiable ranking function using effective smoothing functions. kernel rank directly maximizes a 1-D quality measure of ROC, i.e., AUC (area under the ROC). It exploits the kernel density estimation to model the ranking score distributions and approximate the correct ranking count. The ranking metric is then derived and the learnable parameters are naturally embedded. To address the issues of computation and memory in learning, an efficient implementation is developed based on the gradient descent algorithm. We apply kernel rank with two types of kernel density functions to train the linear discriminant function and the Gaussian mixture model classifiers. From our experiments carried out on the development set for TREC Video Retrieval 2005, we conclude that (1) kernel rank is capable of training any differentiable classifier with various kernels; and (2) the learned ranking function performs better than traditional maximization likelihood or classification error minimization based algorithms in terms of AUC and average precision (AP).

There have been increasing concerns about the security issues of wireless transmission of multimedia in recent years. Wireless networks, by their nature, are more vulnerable to external intrusions than wired ones. Many applications demand authenticating the integrity of multimedia content delivered wirelessly. In this work, we describe a framework for jointly coding and authenticating multimedia to be delivered over heterogeneous wireless networks. We firstly introduce a novel concept called unequal authenticity protection (UAP), which unequally allocate resources to achieve an optimal authentication result. We then consider integrating UAP with specific source and channel-coding models, to obtain optimal end-to-end quality by the means of joint source-channel-authentication analysis. Lastly, we present an implementation of the proposed joint coding and authentication system on a progressive JPEG coder. Experimental results demonstrate that the proposed approach is indeed able to achieve the desired authentication of multimedia over wireless networks

This paper proposes an optimized content-aware authentication scheme for JPEG-2000 streams over lossy networks, where a received packet is consumed only when it is both decodable and authenticated. In a JPEG-2000 codestream, some packets are more important than others in terms of coding dependency and image quality. This naturally motivates allocating more redundant authentication information for the more important packets in order to maximize their probability of authentication and thereby minimize the distortion at the receiver. Towards this goal, with the awareness of its corresponding image content, we formulate an optimization framework to compute an authentication graph to maximize the expected media quality at the receiver, given specific authentication overhead and knowledge of network loss rate. System analysis and experimental results demonstrate that the proposed scheme achieves our design goal in that the rate-distortion (R-D) curve of the authenticated image is very close to the R-D curve when no authentication is required

In this paper, we propose an optimized content-aware authentication scheme for JPEG-2000 streams over lossy networks, where a received packet is consumed only when it is both decodable and authentic. In a JPEG-2000 codestream some packets are more important than others in terms of coding dependency and visual quality. This inspires us to allocate more redundant authentication information for the more important packets to minimize the distortion of the authenticated image at the receiver. In other words, with the awareness of image content, we formulate an optimization framework, which is able to build an authentication graph yielding the best visual quality at the receiver, given a specific authentication overhead and network condition. Experimental results demonstrate that the proposed scheme achieved our design goals in that the R-D curve of an authenticated image is very close to its original one where no authentication is applied.

In the paper we present a generalized discriminative multiple instance learning algorithm (GD-MIL) for multimedia semantic concept detection. It combines the capability of the MIL for automatically weighting the instances in the bag according to their relevance to the positive and negative classes, the expressive power of generative models, and the advantage of discriminative training. We evaluate the GD-MIL on the development set of TRECVID 2005 for high-level feature extraction task. The significant improvement is observed using the GD-MIL over the benchmark. The mean of AP's over 10 concepts using the GD-MIL is 4.18% on the validation set and 3.94 % on the evaluation set. As the comparison, they are 2.12% and 2.63% for the benchmark, correspondingly.

Stream authentication methods usually impose overhead and dependency among packets. The straightforward application of state-of-the-art rate-distortion (R-D) optimized streaming techniques produce highly sub-optimal R-D performance for authenticated video, since they do not account for the additional dependencies. This paper proposes an R-D optimized streaming technique for authenticated video, by accounting for authentication dependencies and overhead. It schedules packet transmission based on packets' importance in terms of both video quality and authentication dependencies. The proposed technique works with any stream authentication method as long as the verification probability can be quantitatively computed from packet loss probability. Simulation results based on H.264 JM 10.1 and NS-2 demonstrate that the proposed authentication-aware R-D optimized streaming technique substantially outperforms authentication-unaware R-D optimized streaming techniques. In particular, when the channel capacity is below the source rate, the PSNR of authenticated video quickly drops to unacceptable levels using conventional R-D optimized streaming techniques, while the proposed technique still maintains R-D optimized video quality.

In this paper, we present an AUC (i.e., the area under the curve of receiver operating characteristics (ROC)) maximization based learning algorithm to design the classifier for maximizing the ranking performance. The proposed approach trains the classifier by directly maximizing an objective function approximating the empirical AUC metric. Then the gradient descent based method is applied to estimate the parameter set of the classifier. Two specific classifiers, i.e. LDF (linear discriminant function) and GMM (Gaussian mixture model), and their corresponding learning algorithms are detailed. We evaluate the proposed algorithms on the development set of TRECVID '05 for semantic concept detection task. We compare the ranking performances with other classifiers trained using the ML (maximum likelihood) or other error minimization methods such as SVM. The results of our proposed algorithm outperform ML and SVM on all concepts in terms of its significant improvements on the AUC or AP (average precision) values. We therefore argue that for semantic concept detection, where ranking performance is much interested than the classification error, the AUC maximization based classifiers are preferred

We propose in this paper a novel content-aware stream-level approach to authenticating multimedia data transmitted over wireless networks. The proposed approach is fundamentally different from conventional authentication methods and offers robust authentication for multimedia data in the presence of channel noise. The scheme is designed in such a way that it facilitates explicit capture and exploitation of channel condition as well as how the multimedia content is packetized and transmitted. The design allows the integration of authentication with the framework of joint source and channel coding (JSCC) to achieve adaptiveness to the content and efficient utilization of limited bandwidth. We have realized the proposed scheme through optimal resource allocation and authentication graph construction. Experiment results demonstrated the effectiveness of this novel approach

This paper proposes a content-aware authentication scheme optimized to account for distortion and overhead for media streaming. When authenticated media is streamed over a lossy network, a received packet is consumed only when it is both decodable and authenticated. In most media formats, some packets are more important than others. This naturally motivates allocating more redundant authentication information for the more important packets in order to maximize their probability of authentication and thereby minimize distortion at the receiver. Toward this goal, with awareness of the media content, we formulate an optimization framework to compute an authentication graph to maximize the expected media quality at the receiver, given specific authentication overhead and knowledge of network loss rates. Experimental results with JPEG-2000 coded images demonstrate that the proposed method achieves our design goal in that the R-D curve of the authenticated image is very close to the R-D curve when no authentication is required

Detecting the presence of musical sounds in broadcast audio is important for content-based indexing and retrieval of auditory and visual information in radio and TV programs. In this paper, we propose a novel approach for musical sounds detection in broadcast audio based on the analysis of the characteristic feature of musical tones, pitch tuning. A spectral analysis method is presented for detecting the evidence of pitch tuning in the audio signal. Unlike the existing methods for discriminating speech and music, the proposed technique is not limited by inadequate training data, and it can deal with the case of music mixed with speech. In addition, the technique can be efficiently implemented for real-time application. Experiments based on TRECVID data set have shown good performance of the proposed technique

The emergence of digital imaging applications is accelerating the need for security of digital imagery. The emerging international standard ISO/IEC JPEG-2000 security (JPSEC) is designed to provide security for digital imagery, and in particular digital imagery coded with the JPEG-2000 image coding standard. This paper provides an overview of the JPSEC standard, including a description of its basic architecture and examples of its use.

This paper presents a new notion of authenticating degraded multimedia content streamed over wireless networks - unequal authenticity protection (UAP). Multimedia content differs from other data in that the importance of different bits within a bitstream often varies. Therefore, given limited resources, a natural solution is to apply better authenticity protection to more important bits, and vice versa. In this paper, a quantitative relationship between the optimal authentication probability and the given resource budget is firstly derived, followed by a proposed authentication graph which realizes the idea of UAP. Simulation results further confirm the validity of the proposal.

Streaming variable-bit-rate video over wide-area networks has many challenging problems due to the great variation of network conditions. In this paper, we focus on the problem of the efficient utilization of the precious network bandwidth in such streaming systems. We propose a novel piggyback prefetching mechanism to improve the bandwidth utilization. We achieve this goal by making use of the unoccupied bandwidth in ongoing streams to deliver some prefetched data and use them to serve future requests. This idea can be applied in various different ways to improve bandwidth utilization. Experimental results show that our mechanism not only can improve bandwidth utilization significantly, but also it can improve the caching performance of the proxy servers in the streaming systems

Automatic music genre classification is one of the most challenging problems in music information retrieval and management of digital music database. In this paper, we propose a new framework using text category methods to classify music genres. This framework is different from current methods for music genre classification. In our framework, we consider music as text-like semantic music document, which is represented by a set of music symbol lexicons with a HMM (hidden Markov models) cluster. Music symbols can be seemed as high-level features or semantic features like beats or rhythms. We use latent semantic indexing (LSI) technique that is widely adopted in text categorization for music genre classification. From the experimental results, we could achieve an average recall over 70% for ten musical genres

In this paper, we describe a configurable content-based MPEG video authentication scheme, which is robust to typical video transcoding approaches, namely frame resizing, frame dropping and requantization. By exploiting the synergy between cryptographic signature, forward error correction (FEC) and digital watermarking, the generated content-based message authentication code (MAC or keyed crypto hash) is embedded back into the video to reduce the transmission cost. The proposed scheme is secure against malicious attacks such as video frame insertion and alteration. System robustness and security are balanced in a configurable way (i.e., more robust the system is, less secure the system will be). Compressed-domain process makes the scheme computationally efficient. Furthermore, the proposed scheme is compliant with state-of-the-art public key infrastructure. Experimental results demonstrate the validity of the proposed scheme

Semi-fragile watermarking techniques aim at detecting malicious manipulations on an image, while allowing acceptable manipulations such as lossy compression. Although both of these manipulations are considered to be pixel value changes, semi-fragile watermarks should be sensitive to malicious manipulations but robust to the degradation introduced by lossy compression and other defined acceptable manipulations. In this paper, after studying the characteristics of both natural images and malicious manipulations, we propose two new semi-fragile authentication techniques robust against lossy compression, using random bias and nonuniform quantization, to improve the performance of the methods proposed by Lin and Chang.

In this paper, we describe a scalable (i.e., lossy-to-lossless) watermarking scheme which overcomes the problem of non-invertible distortion introduced by the watermark signal. The scheme is based on a standardized scalable audio coder (R.S. Yu, et al, 2004) -as a result, the embedded watermark inherits the scalability of the audio coder. We elaborate how the scalability can be used to realize the recovery of the lossless audio signal after watermark embedding. The experimental results demonstrate the validity of the proposed watermarking scheme in terms of robustness, data expansion and perceptual quality.

A blind print-scan (PS) resilient watermarking scheme is proposed in this paper. By employing a series of novel solutions in block classification and different block-based embedding strategies, we achieved a good performance in terms of watermark capacity, robustness and image quality. The experimental results further demonstrate the validity of our proposed scheme.

In this paper, we present a new entropy coder, context-based bit plane Golomb coder (CB-BPGC) for scalable image coding, which achieves better coding performance with lower complexity compared to the state-of-the-art JPEG 2000 entropy coder EBCOT. Because of the direct output lazy bit planes, applying the partial decoding on the corrupted bit planes, and the better compression ratio which may lead to corruption to the less important codestream, CB-BPGC appears more resilient to errors when simulated on the wireless channel based on Rayleigh fading model.

In this paper, we present a scalable (i.e. lossy-to-lossless) watermark scheme based on a recently standardized scalable audio coder-AAZ (R.S. Yu, et al., 2004). The proposed framework enables the recovery of the original lossless audio after watermark embedding, and in the meanwhile, is able to make the watermark adaptive such that the watermark distortion to the lossy host audio is minimized. An encryption mechanism is further employed for restricting unauthorized access to lossless audio and watermark removal. Based on this framework, we elaborate its possible applications on high-quality audio archiving and streaming. Experimental results demonstrate the validity of our proposal.

In line with the recent call for technology on Image Based Authentication (IBA) in JPEG committee [1], we present a novel graphical password design in this paper. It rests on the human cognitive ability of association-based memorization to make the authentication more user-friendly, comparing with traditional textual password. Based on the principle of zero-knowledge proof protocol, we further improve our primary design to overcome the shoulder-surfing attack issue without adding any extra complexity into the authentication procedure. System performance analysis and comparisons are presented to support our proposals.

In a typical content and watermarking based image authentication approach, a feature is extracted from the given image, and then embedded back into the image using a watermarking method. Since the entropy of the feature might be higher than the capacity of the watermarking scheme, or the feature is represented in a continuous domain, it has to be further quantized before embedding. The lost of information during quantization potentially degrades the overall performance of the authentication scheme. This paper propose a simple but effective approach that avoids the feature quantization by additive feature: the feature is firstly added into the image before watermark embedding, and latterly subtracted from the watermarked image. In our experiments, the proposed approach obtains larger achievable robustness/sensitivity region and has a smaller fuzzy region of authenticity than the typical approach.

In this paper, we propose a butterfly-graph based stream authentication scheme for lossy networks where the streaming packets could be lost in both random and burst ways. Due to the nice properties of butterfly graph, the proposed scheme is quite robust and efficient. Theoretical analysis and simulation results show that the proposed scheme outperforms existing schemes in terms of overhead and authentication probability while maintaining the same levels of sender/receiver delay and robustness.

Compression ratio and computational complexity are two major factors for a successful image coder. By exploring the Laplacian distribution of the wavelet coefficients, a new bit plane entropy coder is proposed in this paper. Compared with the state-of-the-art JPEG2000 entropy coder (EBCOT), the proposed coder achieves a 0.75% better loss less performance for 5 level 5/3 wavelet decomposition at block size 64 £ 64 and 2.56% at block size 16 £ 16. Experimental results also show PSNR improvements of about 0.13dB at 1bpp and 0.25dB at 2bpp on average for lossy compression. However, the gain in coding performance is not based on increasing computational complexity but in stead a reduction by using a static arithmetic coder which avoids complicated adaptive procedure.

This paper presents a novel approach for content-based analysis of karaoke music, which utilizes multimodal contents including synchronized lyrics text from the video channel and original singing audio as well as accompaniment audio in the two audio channels. We proposed a novel video text extraction technique to accurately segment the bitmaps of lyrics text from the video frames and track the time of its color changes that are synchronized to the music. A technique that characterizes the original singing voice by analyzing the volume balance between the two audio channels is also proposed. A novel music structure analysis method using lyrics text and audio content is then proposed to precisely identify the verses and choruses of a song, and segment the lyrics into singing phrases. Experimental results based on 20 karaoke music titles of difference languages have shown that our proposed video text extraction technique can detect and segment the lyrics texts with accuracy higher than 90%, and the proposed multimodal approach for music structure analysis method has better performance than the previous methods that are based only on audio content analysis.

Browsing a digital video library can be very tedious especially with an ever expanding collection of multimedia material. We present a novel framework for extracting sub-window shots from MPEG encoded news video with the expectation that this will be another tool that can be used by retrieval systems. Sub-windows shots are also useful for tying in relevant material from multiple video sources. The system makes use of Macroblock parameters to extract visual features, which are then combined to identify possible sub-windows in individual frames. The identified sub-widows are then filtered by a non-linear Spatial-Temporal filter to produce sub-window shots. By working only on compressed domain information, this system avoids full frame decoding of MPEG sequences and hence achieves high speeds of up to 11 times real time.

In this paper, we propose a new method for music identification based on embedded hidden Markov model (EHMM). Differing from conventional HMM, the EHMM estimates the emission probability of its external HMM from the second, state specific HMM, which is referred as internal HMM. EHMM clusters the feature blocks with its external HMM and describes spectral and the temporal structures of each feature block with its internal HMM. Our analysis and experimental results show that the proposed method for music identification achieves higher accuracy and lower complexity than previous approaches

In this paper, a print-and-scan (PS) resilient watermarking scheme is proposed after studying the properties of the PS processing. It is a blind, DCT domain-based scheme with low computation complexity, large capacity. Therefore, it could be easily incorporated into many commercial applications

In this paper, we present a secure and robust content-based digital signature scheme for verifying the authenticity of JPEG2000 images quantitatively, in terms of a unique concept named lowest authenticable bit rates (LABR). Given a LABR, the authenticity of the watermarked JPEG2000 image will be protected as long as its final transcoded bit rate is not less than the LABR. The whole scheme, which is extended from the crypto data-based digital signature scheme, mainly comprises signature generation/verification, error correction coding (ECC) and watermark embedding/extracting. The invariant features, which are generated from fractionalized bit planes during the procedure of embedded block coding with optimized truncation in JPEG2000, are coded and signed by the sender's private key to generate one crypto signature (hundreds of bits only) per image, regardless of the image size. ECC is employed to tame the perturbations of extracted features caused by processes such as transcoding. Watermarking only serves to store the check information of ECC. The proposed solution can be efficiently incorporated into the JPEG2000 codec (Part 1) and is also compatible with Public Key Infrastructure. After detailing the proposed solution, system performance on security as well as robustness will be evaluated.

In this paper, a blind object-based video watermarking scheme, which is robust to MPEG4 compression and normal video editing such as Rotation Scaling or Translation (RST) is proposed. The watermark is embedded into Log-Polar Mapping (LPM) of DFT magnitude of the object; the synchronisation problem introduced by RST is solved using PCA (Principle Component Analysis): by mapping the difference between watermarked video object and original video object from LPM domain to DFT domain, the watermarking can eventually be performed in the DFT domain so that video quality degradation caused by LPM and Inverse LPM (ILPM) is minimized. Experimental results verified the validity of the proposed robust watermarking scheme, which also has large watermark capacity.

Edges in a natural image have important effects on the subjective visual quality. During the transmissions of wavelet-compressed images such as JPEG2000, errors in high frequency subbands will result in the effects like ring or ripple artifacts around edges. In this paper, we propose an error concealment algorithm to remove these annoying artifacts. This algorithm requires an edge directed filter. Although some known filters can be employed, we tailor-make a new edge directed filter which fits well in our algorithm. The proposed scheme firstly enhances the received damaged image using the edge directed filter. Then the recovered wavelet coefficients are rectified using two constraint functions, which are based on the statistical characteristics in the wavelet domain and the observation that correctly received data must remain unchanged. Simulation results show that the image quality has been significantly improved in terms of both objective and subjective evaluation.

In this paper, we propose a lossy-to-lossless watermarking scheme for JPEG2000/J2K images. The watermarking is incorporated into the procedure of J2K coding in a way that the final watermark coded J2K bitstream can still maintain lossless-to-lossy scalability. To recover the original image, the J2K decoder does not need to extract the embedded watermark. This is achieved by dividing the magnitude bits of the wavelet coefficients into two portions, controlled by the watermarking survival rate (WSR). The upper portion is used to embed watermarks, while the lower portion is modified to compensate for the distortion introduced by the watermarks in the upper portion. Watermark detection can be done either from the upper portion or from the lower portion, based on a presetting threshold. Experimental results show that the J2K coded watermarked images still nearly follow the rate-distortion curve optimized for their original J2K images.

Searching for similar video clips in large video database, or video identification, requires finding the nearest neighbor in high-dimensional feature space. Locality sensitive hashing, or LSH, is a well-known indexing method that allows us to efficiently find approximate nearest neighbor in such space. We address two weaknesses of LSH when applied to the video identification problem. We propose two enhancements to LSH, and show that our enhancements improve the performance of LSH significantly in terms of efficiency and accuracy.

Recently, among various data hiding techniques, a new subset, lossless data hiding, has drawn tremendous interest. Most existing lossless data hiding algorithms are, however, fragile in the sense that they can be defeated when compression or other small alteration is applied to the marked image. The method of C. De Vleeschouwer et al. (see IEEE Trans. Multimedia, vol.5, p.97-105, 2003) is the only existing semi-fragile lossless data hiding technique (also referred to as robust lossless data hiding), which is robust against high quality JPEG compression. We first point out that this technique has a fatal problem: salt-and-pepper noise caused by using modulo 256 addition. We then propose a novel robust lossless data hiding technique, which does not generate salt-and-pepper noise. This technique has been successfully applied to many commonly used images (including medical images, more than 1000 images in the CorelDRAW database, and JPEG2000 test images), thus demonstrating its generality. The experimental results show that the visual quality, payload and robustness are acceptable. In addition to medical and law enforcement fields, it has been applied to authenticate losslessly compressed JPEG2000 images.

With the ambient use of digital images and the increasing concern on their integrity and originality, consumers are facing an emergent need of authenticating degraded images despite lossy compression and packet loss. In this paper, we propose a scheme to meet this need by incorporating a watermarking solution into a traditional crypto signature scheme to make the digital signatures robust to image degradations. The proposed approach is compatible with traditional crypto signature schemes except that the original image needs to be watermarked in order to guarantee the robustness of its derived digital signature. We demonstrate the effectiveness of this proposed scheme through practical experimental results as well as illustrative analysis.

We propose an error concealment algorithm for JPEG2000 image transmissions over unreliable channels. Firstly, the local principal edge information in the damaged area is detected in the spatial domain. Then the proposed orthogonal edge directed filters (OEDFs) are applied to remove the ring or ripple artifact errors due to the loss of some wavelet transform (WT) bitplane data. Two kinds of constraints in WT domain are used for rectifying the recovered WT coefficients obtained from OEDFs, namely the WT known-value constraint and the empirical statistical constraint of the WT coefficients. Finally, this filtering-and-rectifying procedure is iterated until convergent. Simulation results have shown that both objective and subjective image quality have been improved by our proposed algorithm.

This work proposes a unified authentication framework for JPEG2000 images, which consists of fragile, lossy and lossless authentication for different applications. The authentication strength can be specified using only one parameter called lowest authentication bit-rate (LABR), bringing much convenience to users. The lossy and lossless authentication could survive various incidental distortions while being able to allocate malicious attacks. In addition, with lossless authentication, the original image can be recovered after verification if no incidental distortion is introduced.

This paper presents an object based watermarking solution for MPEG4 video authentication. The watermark is embedded in the discrete Fourier transform (DFT) domain before MPEG4 encoding. Groups of DFT coefficients in the low-middle frequency band are selected for watermarking. The coefficients in every group are divided into two sub-groups based on a pre-defined pattern, and the energy relationship between these two sub-groups is used to hide the watermark. The experimental results show that our algorithm is robust against MPEG4 compression as well as object based video manipulations such as rotation, scaling and inaccurate segmentation.

In this paper, we present a secure and robust content authentication scheme for scalable video streaming. In our authentication scheme we consider three common video transcoding methods as acceptable content manipulations, when the streaming bit-rate needs to be reduced, namely frame resizing, frame dropping and multi-cycle coding. By employing error correction coding (ECC) in different ways, the proposed scheme is insensitive to those incidental distortions introduced during the transcoding (i.e., robust) while is still sensitive to other intentional distortions such as frame alterations and insertion (i.e., secure). One key feature in our scheme is that it achieves an end-to-end authentication independent of transcoding infrastructure and obtains a good compromise between system robustness and security.

We present a generic content-based solution targeting at authenticating image in a semi-fragile way, which integrates watermarking-based approach with signature-based approach. Robust signatures are cryptographically generated based on invariant features called significance-linked connected component (SLCC) extracted from image content and are then signed and embedded back into the image again as watermarks, all in the wavelet domain. De-noising and morphological filtering are applied as pre-processing to tame some small perturbations on extracted features caused by various incidental distortions introduced in acceptable manipulations such as lossy compression, common image processing (bluffing, sharpening, etc.) as well as watermarking. Error correcting coding is employed to further bridge between generated signatures and watermarks in a novel way: message bits are formed based on SLCC features, and parity check bits are taken as the seeds of watermarks. The generated signature is hashable and can be incorporated into a PKI framework.

We propose a novel integrated approach to quantitative semi-fragile authentication of JPEG2000 images under a generic framework which combines ECC and PKI infrastructures. Firstly acceptable manipulations (e.g., re-encoding) which should pass authentication are defined based on considerations of some target applications. We propose a unique concept of lowest authenticable bit rate - images undergoing repetitive re-encoding are guaranteed to pass authentication provided the re-encoding rates are above the lowest allowable bit rate. Our solutions include computation of content-based features derived from the EBCOT encoding procedure of JPEG2000, error correction coding of the derived features, PKI cryptographic signing, and finally robust embedding of the feature codes into image as watermarks.

Automatic face location in complex scenes is extremely challenging in human face recognition systems. Further more, the facial features detection also plays an important role. The paper presents a scheme for robust face and eyes detection from an image. The scheme uses the Gaussian steerable filter to search and detect the facial feature (preattentive feature) roughly in an image. The face model is investigated to locate the whole face and facial features, such as eyes, nose and mouth. Here, multiple evidences are used in the face location and eyes detection. One important feature is the structural information of the face, i.e. facial components of certain structure. The other is the symmetry property of the face, here only the front face with certain pose variation is considered. It will reduce the computation greatly. For facial components detection, some image features and PCA features are used for verification from the candidates detected before. Experiments show that the algorithm is robust and fast.