Federated learning (FL) has shown success in collaboratively training a model among decentralized data resources without directly sharing privacy-sensitive training data. Despite recent advances, non-IID (non-independent and identically distributed) data poses an inevitable challenge that hinders the use of FL. In this work, we address the issue of non-IID histopathological images with feature distribution shifts from an intuitive perspective that has only received limited attention. Specifically, we address this issue from the perspective of data distribution by solely adjusting the data distributions of all clients. Building on the success of diffusion models in fitting data distributions and leveraging stain separation to extract the pivotal features that are closely related to the non-IID properties of histopathological images, we propose a Federated Stain Distribution Alignment (FedSDA) method. FedSDA aligns the stain distribution of each client with a target distribution in an FL framework to mitigate distribution shifts among clients. Furthermore, considering that training diffusion models on raw data in FL has been shown to be susceptible to privacy leakage risks, we circumvent this problem while still effectively achieving alignment. Extensive experimental results show that FedSDA is not only effective in improving baselines that focus on mitigating disparities across clients’ model updates but also outperforms baselines that address the non-IID data issues from the perspective of data distribution. We show that FedSDA provides valuable and practical insights for the computational pathology community.

Face recognition poses serious privacy risks due to its reliance on sensitive and immutable biometric data. While modern systems mitigate privacy risks by mapping facial images to embeddings (commonly regarded as privacy-preserving), model inversion attacks reveal that identity information can still be recovered, exposing critical vulnerabilities. However, existing attacks are often computationally expensive and lack generalization, especially those requiring target-specific training. Even training-free approaches suffer from limited identity controllability, hindering faithful reconstruction of nuanced or unseen identities. In this work, we propose DiffMI, the first diffusion-driven, training-free model inversion attack. DiffMI introduces a novel pipeline combining robust latent code initialization, a ranked adversarial refinement strategy, and a statistically grounded, confidence-aware optimization objective. DiffMI applies directly to unseen target identities and face recognition models, offering greater adaptability than training-dependent approaches while significantly reducing computational overhead. Our method achieves 84.42%–92.87% attack success rates against inversion-resilient systems and outperforms the best prior training-free GAN-based approach by 4.01%–9.82%. The implementation is available at https://github.com/azrealwang/DiffMI.

Federated learning for pathological whole slide image (WSI) classification allows multiple clients to train a global multiple instance learning (MIL) model without sharing their privacy-sensitive WSIs. To accommodate the non-independent and identically distributed (non-i.i.d.) feature shifts, cross-client style transfer has been popularly used but is subject to two fundamental issues: (1) WSI contains multiple morphological structures, each corresponding to a distinct style. (2) Performing style transfer may potentially shift the region of interests (RoIs) in the augmented WSIs. To address these challenges, we propose HistoFS, a federated learning framework for computational pathology on non-i.i.d. feature shifts in WSI classification. Specifically, we introduce pseudo bag styles that capture multiple style variations within a single WSI. In addition, an authenticity module is introduced to ensure that RoIs are preserved, allowing local models to learn WSIs with diverse styles while maintaining essential RoIs. Extensive experiments validate the superiority of HistoFS over state-of-the-art methods on three clinical datasets. Our code is available at https://lalakitchen.github.io/HistoFS/.

Visual State Space Models (VSSM) have shown remarkable performance in various computer vision tasks. However, backdoor attacks pose significant security challenges, causing compromised models to predict target labels when specific triggers are present while maintaining normal behavior on benign samples. In this paper, we investigate the robustness of VSSMs against backdoor attacks. Specifically, we delicately design a novel framework for VSSMs, dubbed BadVim, which utilizes low-rank perturbations on state-wise to uncover their impact on state transitions during training. By poisoning only 0.3% of the training data, our attacks cause any trigger-embedded input to be misclassified to the targeted class with a high attack success rate (over 97%) at inference time. Our findings suggest that the state-space representation property of VSSMs, which enhances model capability, may also contribute to its vulnerability to backdoor attacks. Our attack exhibits effectiveness across three datasets, even bypassing state-of-the-art defenses against such attacks. Extensive experiments show that the backdoor robustness of VSSMs is comparable to that of Transformers (ViTs) and superior to that of Convolutional Neural Networks (CNNs). We believe our findings will prompt the community to reconsider the trade-offs between performance and robustness in model design.

The growing collection of personal voice data online has heightened the demand for effective privacy protection through speaker de-identification. While existing anonymization methods successfully obscure speaker identity, they fail to simultaneously achieve robust identity protection, natural speech preservation, and flexible user customization. We address this limitation through three key innovations: (1) a dynamic neural style-transfer framework that generates perceptually natural yet anonymized speech via reference-guided interpolation; (2) a privacy-preserving disentanglement technique using a triple-encoder architecture to suppress speaker identity while preserving linguistic content and transferable prosodic feature; and (3) a user-customizable design that supports intentional voice persona modulation, catering to emerging existing methods in anonymization effectiveness while maintaining superior speech quality and adaptability. Experimental results and comparisons demonstrate the effectiveness of our method, effectively bridging the gap between privacy protection and speech utility in real-world applications.

The great success of the diffusion model in image synthesis led to the release of gigantic commercial models, raising the issue of copyright protection and inappropriate content generation. Training-free diffusion watermarking provides a low-cost solution for these issues. However, the prior works remain vulnerable to rotation, scaling, and translation (RST) attacks. Although some methods employ meticulously designed patterns to mitigate this issue, they often reduce watermark capacity, which can result in identity (ID) collusion. To address these problems, we propose MaXsive, a training-free diffusion model generative watermarking technique that has high capacity and robustness. MaXsive best utilizes the initial noise to watermark the diffusion model. Moreover, instead of using a meticulously repetitive ring pattern, we propose injecting the X-shape template to recover the RST distortions. This design significantly increases robustness without losing any capacity, making ID collusion less likely to happen. The effectiveness of MaXsive has been verified on two well-known watermarking benchmarks under the scenarios of verification and identification.

The effectiveness of domain translation in addressing image-based problems of Unsupervised Domain Adaptation (UDA) depends on the quality of the translated images and the preservation of crucial discriminative features. However, achieving high-quality and stable translations typically requires paired data, which poses a challenge in scenarios with limited annotations in the target domain. To address this issue, this paper proposes a novel method termed Stain-Guided Cycle Diffusion (SGCD), employing a dual diffusion model with bidirectional generative constraints to synthesize highly realistic data for downstream task fine-tuning. The bidirectional generative constraints ensure that the translated images retain the features critical to the downstream model in properly controlling the generation process. Additionally, a stain-guided consistency loss is introduced to enhance the denoising capability of the dual diffusion model, thereby improving the quality of images translated between different domains using latents from one domain and a diffusion model trained on another. Experiments conducted on four public datasets demonstrate that SGCD can effectively enhance the performance of downstream task models on the target domain.

Large Language Models (LLMs) are increasingly adopted in high-stakes scenarios, yet their safety mechanisms often remain fragile. Simple jailbreak prompts or even benign fine-tuning can bypass internal safeguards, underscoring the need to understand the failure modes of current safety strategies. Recent findings suggest that vulnerabilities emerge when alignment is confined to only the initial output tokens. To address this, we introduce the notion of safety depth, a designated output position where the model refuses to generate harmful content. While deeper alignment appears promising, identifying the optimal safety depth remains an open and underexplored challenge. We leverage the equivalence between autoregressive language models and Markov chains to derive the first theoretical result on identifying the optimal safety depth. To reach this safety depth effectively, we propose a cyclic group augmentation strategy that improves safety scores across six LLMs. In addition, we uncover a critical interaction between safety depth and ensemble width, demonstrating that larger ensembles can offset shallower alignments. These results suggest that test-time computation, often overlooked in safety alignment, can play a key role. Our approach provides actionable insights for building safer LLMs.

Semi-supervised learning (SSL) has achieved remarkable performance with a small fraction of labeled data by leveraging vast amounts of unlabeled data from the Internet. However, this large pool of untrusted data is extremely vulnerable to data poisoning, leading to potential backdoor attacks. Current backdoor defenses are not yet effective against such a vulnerability in SSL. In this study, we propose a novel method, Unlabeled Data Purification (UPure), to disrupt the association between trigger patterns and target classes by introducing perturbations in the frequency domain. By leveraging the Rate-Distortion-Perception (RDP) trade-off, we further identify the frequency band, where the perturbations are added, and Justify this selection. Notably, UPure purifies poisoned unlabeled data without the need of extra clean labeled data. Extensive experiments on four benchmark datasets and five SSL algorithms demonstrate that UPure effectively reduces the attack success rate from 99.78% to 0% while maintaining model accuracy. Code is available here: https://github.com/chengyi-chris/UPure.

Deep neural networks are highly vulnerable to adversarial examples, which are inputs with small, carefully crafted perturbations that cause misclassification—making adversarial attacks a critical tool for evaluating robustness. Existing black-box methods typically entail a trade-off between precision and flexibility: pixel-sparse attacks (e.g., single- or few-pixel attacks) provide fine-grained control but lack adaptability, whereas patch- or frequency-based attacks improve efficiency or transferability, but at the cost of producing larger and less precise perturbations. We presentGreedyPixel, a fine-grained black-box attack method that performsbrute-force-style, per-pixel greedy optimizationguided by a surrogate-derived priority map and refined by means of query feedback. It evaluates each coordinate directlywithout any gradient information, guaranteeing monotonic loss reduction and convergence to a coordinate-wise optimum, while also yielding near white-box-level precision and pixel-wise sparsity and perceptual quality. On the CIFAR-10 and ImageNet datasets, spanning convolutional neural networks (CNNs) and Transformer models, GreedyPixel achieved state-of-the-art success rates with visually imperceptible perturbations, effectively bridging the gap between black-box practicality and white-box performance. The implementation is available at https://github.com/azrealwang/greedypixel.

The difficulty of collecting histopathology whole slide images (WSIs) and lack of disease-positives within slide image is a major obstacle to the development of computer-aided diagnosis. Existing works suggest sharing knowledge learned by mimicking the discriminative features, in which a student model with lack-features is trained to mimic a teacher model with rich-features. However, most feature mimicking methods, designed for natural image tasks, might be failed in the case of whole slide images. We propose a new method to mimic features for knowledge sharing in WSI classification. On the one hand, attention guided feature selection and normalization is proposed to extract discriminative features from a learning model and use attention scores to quantify feature contributions so as to identify the diseases-positive regions (a.k.a Region of Interests). On the other hand, we propose to learn by mimicking the high-discriminative features based on disease-positive regions. Our method is evaluated on two datasets with rich-features and two datasets with lack-features. Results demonstrate that our proposed method can boost performance compared to competitive MIL and knowledge sharing methods at the WSI level.

Deep neural networks (DNNs) are known to be vulnerable to backdoor attacks. Specifically, the attacker endeavors to implant backdoors in the DNN model by injecting a set of poisoning samples such that the malicious model predicts target labels once the backdoor is triggered. The clean-image attack has recently emerged as a threat in multi-label classification, where an attacker is able to poison training labels without tampering with image contents. In this paper, we propose a simple but effective method to alleviate clean-image backdoor attacks. Considering the difference in weight convergence between the benign model and backdoor model, our method relies on partial weight initialization and fine-tuning to mitigate the backdoor behaviors of a suspicious model. The fine-tuned model sustains its clean accuracy through knowledge distillation over a few iterations. Importantly, our approach does not require extra clean images for purification. Extensive experiments demonstrate the effectiveness of our defenses against clean-image attacks for multi-label classifications across two benchmark datasets.

The proliferation of deepfakes has posed significant challenges to identity authentication and content integrity. Consequently, the field of deepfake detection has garnered considerable attention, with numerous researchers proposing detection methods to discern between genuine and fake images. In this paper, we investigate a scenario wherein deepfake detection models face the threat of backdoor attacks. Through a comprehensive evaluation of backdoor attacks and defense strategies, we provide an insightful analysis of their outcomes. Our findings reveal that defense methods for complex tasks like deepfake detection may exhibit weaknesses for three reasons. Firstly, in deepfake detection, the common practice of cropping faces from images for detection may lead to triggers being absent in the cropped images. Secondly, deepfake detection is complex, especially when triggers exist, as it demands the model to learn subtle features. Lastly, the number of classes in a classification task is crucial for developing defenses against backdoor attacks. This study advances our understanding of backdoor attacks within the context of deepfake detection, urging the development of more robust defense mechanisms.

The growing diversity of digital face manipulation techniques has led to an urgent need for a universal and robust detection technology to mitigate the risks posed by malicious forgeries. We present a blended-based detection approach that has robust applicability to unseen datasets, seamlessly integrating two key components: a method for generating synthetic training samples, specifically Reconstructed Blended Images, which incorporates potential deepfake generator artifacts; and a detection model for multi-scale feature reconstruction, which is adept at capturing generic boundary artifacts and noise distribution anomalies induced by digital face manipulations. Empirical results demonstrate that this approach results in better performance in both cross-manipulation detection and cross-dataset detection on unseen data.

Backdoor attacks are a significant concern in deep learning, especially in applications where models are trained on data from untrusted sources. Plenty of approaches use latent representations of a backdoor model to separate trigger samples from clean ones. However, these defenses rely on some clean data to train a classifier. Recently, researchers have designed adaptive attacks that are latently inseparable, making it even harder for the defender to prevent backdoor attacks. For these reasons, we propose a novel defense, Higher Moment Disparity (HMD), based on the higher moment inspired by latent statistics. HMD uses no clean data and all intermediate representations to avoid previous concerns. Extensive experiments show that our defense against various attacks is promising.

Traffic sign recognition plays a crucial role in self-driving cars, but unfortunately, it is vulnerable to adversarial patches (AP). Although AP can efficiently fool DNN-based models in previous studies, the connection between image forensics and AP detection still needs to be explored. From a high-level point of view, their goals are the same. That is to find tampered regions and prevent false positives in the meantime. A natural question arises: "Is achieving application-agnostic anomaly detection possible?" In this paper, we propose Image Forensics Defense Against Adversarial Patch (IDAP), a framework to defend against adversarial patches via generalizable features learned from tampered images. In addition, we incorporate the Hausdorff erosion loss into our network model for joint training to complete the shape of a predicted mask. Extensive experimental comparisons on three datasets, including COCO, DFG, and APRICOT demonstrate that IDAP outperforms state-of-the-art AP detection methods.

Real-world medical datasets often suffer from class imbalance, which can lead to degraded performance due to limited samples of the minority class. In another line of research, Transformer-based multiple instance learning (Transformer-MIL) has shown promise in addressing the pairwise correlation between instances in medical whole slide images (WSIs) with gigapixel resolution and non-uniform sizes. However, these characteristics pose challenges for state-of-the-art (SOTA) oversampling methods aiming at diversifying the minority context in imbalanced WSIs.In this paper, we propose an Attention-Guided Prototype Mixing scheme at the WSI level. We leverage Transformer-MIL training to determine the distribution of semantic instances and identify relevant instances for cutting and pasting across different WSI (bag of instances). To our knowledge, applying Transformer is often limited by memory requirements and time complexity, particularly when dealing with gigabyte-sized WSIs. We introduce the concept of prototype instances that have smaller representations while preserving the uniform size and intrinsic features of the WSI.We demonstrate that our proposed method can boost performance compared to competitive SOTA oversampling and augmentation methods at an imbalanced WSI level.

Whole Slide Images (WSIs) are usually gigapixel in size and lack pixel-level annotations. The WSI datasets are also imbalanced in categories. These unique characteristics, significantly different from the ones in natural images, pose the challenge of classifying WSI images as a kind of weakly supervise learning problems. In this study, we propose, RankMix, a data augmentation method of mixing ranked features in a pair of WSIs. RankMix introduces the concepts of pseudo labeling and ranking in order to extract key WSI regions in contributing to the WSI classification task. A two-stage training is further proposed to boost stable training and model performance. To our knowledge, the study of weakly supervised learning from the perspective of data augmentation to deal with the WSI classification problem that suffers from lack of training data and imbalance of categories is relatively un-explored.

Despite an increased demand for valuable data, the privacy concerns associated with sensitive datasets present a barrier to data sharing. One may use differentially private generative models to generate synthetic data. Unfortunately, generators are typically restricted to generating images of low-resolutions due to the limitation of noisy gradients. Here, we propose DPGEN, a network model designed to synthesize high-resolution natural images while satisfying differential privacy. In particular, we propose an energy-guided network trained on sanitized data to indicate the direction of the true data distribution via Langevin Markov chain Monte Carlo (MCMC) sampling method. In contrast to the state-of-the-art methods that can process only low-resolution images (e.g., MNIST and Fashion-MNIST), DPGEN can generate differentially private synthetic images with resolutions up to$128\times 128$with superior visual quality and data utility. Our code is available at https://github.com/chiamuyu/DPGEN

Watermarking neural networks (NNs) for ownership protection has received considerable attention recently. Resisting both model pruning and fine-tuning is commonly considered to evaluate the robustness of a watermarked NN. However, the rationale behind such a robustness is still relatively unexplored in the literature. In this paper, we study this problem to propose a so-called sparse trigger pattern (STP) guided deep learning model watermarking method. We provide empirical evidence to show that trigger patterns are able to make the distribution of model parameters compact, and thus exhibit interpretable resilience to model pruning and fine-tuning. We find the effect of STP can also be technically interpreted as the first layer dropout. Extensive experiments demonstrate the robustness of our method.

With the growing use of camera devices, the industry has many image datasets that provide more opportunities for collaboration between the machine learning community and industry. However, the sensitive information in the datasets discourages data owners from releasing these datasets. Despite recent research devoted to removing sensitive information from images, they provide neither meaningful privacy-utility trade-off nor provable privacy guarantees. In this study, with the consideration of the perceptual similarity, we propose perceptual indistinguishability (PI) as a formal privacy notion particularly for images. We also propose PI-Net, a privacy-preserving mechanism that achieves image obfuscation with PI guarantee. Our study shows that PI-Net achieves significantly better privacy utility trade-off through public image data.

This paper proposes a novel miscellaneous-context-based method to convert a sentence into a knowledge embedding in the form of a directed graph. We adopt the idea of conceptual graphs to frame for the miscellaneous textual information into conceptual compactness. We first empirically observe that this graph representation method can (1) accommodate the slot-filling challenges in typical question answering and (2) access to the sentence-level graph structure in order to explicitly capture the neighbouring connections of reference concept nodes. Secondly, we propose a task-agnostic semantics-measured module, which cooperates with the graph representation method, in order to (3) project an edge of a sentence-level graph to the space of semantic relevance with respect to the corresponding concept nodes. As a result of experiments on the QA-type relation extraction, the combination of the graph representation and the semantics-measured module achieves the high accuracy of answer prediction and offers human-comprehensible graphical interpretation for every well-formed sample. To our knowledge, our approach is the first towards the interpretable process of learning vocabulary representations with the experimental evidence.

Compressed sensing (CS) is a technique which uses fewer measurements than dictated by the Nyquist sampling theorem. The traditional CS with linear measurements achieves effective recovery, but it suffers from large bit consumption due to the precision required by those measurements. Then, the one-bit CS with binary measurements is proposed to save the bit budget, but it is infeasible when the energy information of signals is not available as a prior knowledge. Subsequently, the hybrid CS which combines traditional CS and one-bit CS appears, striking a balance between the pros and cons of both types of CS. Given that one-bit CS is optimal for the direction estimation of signals under noise with a fixed bit budget and that traditional CS is able to provide residue information and estimated signals, we focus on the design of greedy algorithms, which consist of the main steps of support detection and recovered signal updates, for hybrid CS in this paper. We propose two greedy algorithms for hybrid CS, with traditional CS offering signal estimates and updated residues, which help one-bit CS detect the support iteratively. Then, we provide a theoretical analysis of the error bound between the normalized original signal and the normalized estimated signal. Numerical results demonstrate the efficacy of the proposed greedy algorithms for hybrid CS in noisy environments.

Cloud storage services enable individuals and organizations to outsource data storage to remote servers. Cloud storage providers generally adopt data deduplication, a technique for eliminating redundant data by keeping only a single copy of a file, thus saving a considerable amount of storage and bandwidth. However, an attacker can abuse deduplication protocols to steal information. For example, an attacker can perform the duplicate check to verify whether a file (e.g., a pay slip, with a specific name and salary amount) is already stored (by someone else), hence breaching the user privacy. In this paper, we propose ZEUS (zero-knowledge deduplication response) framework. We develop ZEUS and ZEUS+, two privacy-aware deduplication protocols: ZEUS provides weaker privacy guarantees while being more efficient in the communication cost, while ZEUSþ guarantees stronger privacy properties, at an increased communication cost. To the best of our knowledge, ZEUS is the first solution which addresses two-side privacy by neither using any extra hardware nor depending on heuristically chosen parameters used by the existing solutions, thus reducing both cost and complexity of the cloud storage. In summary, through the evaluation on real datasets and comparison to existing solutions, our proposed framework demonstrates its capability of eliminating data deduplication-based side channel and at the same time keeping the deduplication benefits.

Compressive sensing (CS) is proposed for signal sampling below the Nyquist rate based on the assumption that the signal is sparse in some transformed domain. Most sensing matrices (e.g., Gaussian random matrix) in CS, however, usually suffer from unfriendly hardware implementation, high computation cost, and huge memory storage. In this letter, we propose a deterministic sensing matrix for collecting measurements fed into sparse fast Fourier transform (sFFT) as the decoder. Compared with the conventional paradigm with Gaussian random matrix at encoder and convex programming or greedy method at decoders, sFFT can reconstruct sparse signals with very low computation cost under the comparable number of measurements. But, the limitation is that the signal must be sparse in the frequency domain. We further show how to relax this limitation into any domains with the transformation matrix or dictionary being circulant. Experimental and theoretical results validate that the proposed method achieves fast sensing, fast recovery, and low memory cost.

Cost-efficient compressive sensing of large-scale images with quickly reconstructed high-quality results is very challenging. In this paper, we present an algorithm to solve convex optimization via the tree structure sparsity pattern, which can be run in the operator to reduce computation cost and maintain good quality, especially for large-scale images. We also provide convergence analysis and convergence rate analysis for the proposed method. The feasibility of our method is verified through simulations and comparison with the state-of-the-art algorithms.

We address the problem of compressed sensing with multiple measurement vectors associated with prior information in order to better reconstruct an original sparse signal. This problem is modeled via convex optimization with ℓ2,1- ℓ2,1minimization. We establish bounds on the number of measurements required for successful recovery. Our bounds and geometrical interpretations reveal that if the prior information can decrease the statistical dimension and make it lower than that under the case without prior information, ℓ2,1- ℓ2,1minimization improves the recovery performance dramatically. All our findings are further verified via simulations.

Binary embedding of high-dimensional data aims to produce low-dimensional binary codes while preserving discriminative power. State-of-the-art methods often suffer from high computation and storage costs. We present a simple and fast embedding scheme by first downsampling N-dimensional data into M-dimensional data and then multiplying the data with an M×M circulant matrix. Our method requires O(N + M log M) computation and O(N) storage costs. We prove if data have sparsity, our scheme can achieve similarity-preserving well. Experiments further demonstrate that though our method is cost-effective and fast, it still achieves comparable performance in image applications.

Clone detection, aimed at detecting illegal copies with all of the credentials of legitimate sensor nodes, is of great importance for sensor networks because of the severe impact of clones on network operations, like routing, data collection, and key distribution. Various detection methods have been proposed, but most of them are communication-inefficient due to the common use of the witness-finding strategy. In view of the sparse characteristic of replicated nodes, we propose a novel clone detection framework, called CSI, based on a state-of-the-art signal processing technology, compressed sensing. Specifically, CSI bases its detection effectiveness on the compressed aggregation of sensor readings. Due to its consideration of data aggregation, CSI not only achieves the asymptotically lowest communication cost but also makes the network traffic evenly distributed over sensor nodes. In particular, this is achieved by exploiting the sparse property of the clones within the sensor network caused by the clone attack. The performance and security of CSI will be demonstrated by numerical simulations, analyses, and prototype implementation.

In sparse signal recovery of compressive sensing, the phase transition determines the edge, which separates successful recovery and failed recovery. Moreover, the width of phase transition determines the vague region, where sparse recovery is achieved in a probabilistic manner. Earlier works on phase transition analysis in either single measurement vector (SMV) or multiple measurement vectors (MMVs) is too strict or ideal to be satisfied in real world. Recently, phase transition analysis based on conic geometry has been found to close the gap between theoretical analysis and practical recovery result for SMV. In this paper, we explore a rigorous analysis on phase transition of MMVs. Such an extension is not intuitive at all since we need to redefine the null space and descent cone, and evaluate the statistical dimension for ℓ2,1-norm. By presenting the necessary and sufficient condition of successful recovery from MMVs, we can have a boundary on the probability that the solution of a MMVs recovery problem by convex programming is successful or not. Our theoretical analysis is verified to accurately predict the practical phase transition diagram of MMVs.

Cooperative spectrum sensing (CSS) in cognitive radio networks conducts cooperation among sensing users to jointly sense the sparse spectrum and utilize available spectrums. Greedy multiple measurement vectors (MMVs) algorithm in the context of compressed sensing can ideally model the wideband CSS scenario to efficiently solve the support detection problem for identification of occupied channels. Actually, the number of sparsity is unknown, and most of greedy algorithms for MMVs lack for a (robust) stopping criterion of determining when the greedy algorithm should terminate. In this paper, we analyze and derive oracle stopping bounds for greedy MMVs algorithms without depending on prior information such as sparsity. Moreover, we introduce a practical subspace MMVs greedy algorithm that extends from a subspace-based sparse recovery method to a more practical setting, in which no prior information are required. Extensive simulations confirm the feasibility of the proposed stopping criteria and our sparse recovery algorithm.

Sparse Fast Fourier Transform (sFFT) [1][2], has been recently proposed to outperform FFT in reducing computational complexity. Assume that an input signal of length N in the frequency domain is K-sparse, where K ≤ N. sFFT costs O(K logN) instead of O(N logN) in FFT. In this paper, a new fast sFFT algorithm is proposed and costs O(K logK) averagely without any operations being related to N. The idea is to downsample the original input signal at the beginning. Subsequent processing operates under downsampled signals, which length is proportional to O(K). However, downsampling possibly leads to “aliasing.” By shift theorem of DFT, the aliasing problem can be formulated as the “Moment-preserving problem.” In addition, a top-down iterative strategy combined with different downsampling factors further saves computational costs. Complexity analysis and experimental results show that our method outperforms FFT and sFFT.

We deal with the challenging problem of node replication detection. Although defending against node replication attacks demands immediate attention, compared to the extensive exploration on the defense against node replication attacks in static networks, only a few solutions in mobile networks have been presented. Moreover, while most of the existing schemes in static networks rely on the witness-finding strategy, which cannot be applied to mobile networks, the velocity-exceeding strategy used in existing schemes in mobile networks incurs efficiency and security problems. Therefore, based on our devised challenge-and-response and encounter-number approaches, localized algorithms are proposed to resist node replication attacks in mobile sensor networks. The advantages of our proposed algorithms include 1) localized detection; 2) efficiency and effectiveness; 3) network-wide synchronization avoidance; and 4) network-wide revocation avoidance. Performance comparisons with known methods are provided to demonstrate the efficiency of our proposed algorithms. Prototype implementation on TelosB mote demonstrates the practicality of our proposed methods.

Ensuring the security of communication and access control in Wireless Sensor Networks (WSNs) is of paramount importance. In this paper, we present a security mechanism, MoteSec-Aware, built on the network layer for WSNs with focus on secure network protocol and data access control. In the secure network protocol of MoteSec-Aware, a Virtual Counter Manager (VCM) with a synchronized incremental counter is presented to detect the replay and jamming attacks based on the symmetric key cryptography using AES in OCB mode. For access control, we investigate the Key-Lock Matching (KLM) method to prevent unauthorized access. We implement MoteSec-Aware for the TelosB prototype sensor platform running TinyOS 1.1.15, and conduct field experiments and TOSSIM-based simulations to evaluate the performance of MoteSec-Aware. The results demonstrate that MoteSec-Aware consumes much less energy, yet achieves higher security than several state-of-the-art methods.

A large-scale wireless sensor network constructed in terms of two-tiered architecture, where cloud nodes take charge of storing sensed data and processing queries with respect to the sensing nodes and querists, incurs security breach. This is because the importance of cloud nodes makes them attractive to adversaries and raises concerns about data privacy and query result correctness. To address these problems, we propose an efficient approach, namely EQ (efficient query), which mainly prevents adversaries from gaining the information processed by or stored in cloud nodes, and detects the compromised cloud nodes when they misbehave. EQ can not only achieve the goals of data privacy and integrity preserving but also ensure the secure range query without incurring false positive. For data privacy preserving, EQ presents an order encryption mechanism by adopting stream cipher to encrypt/decrypt all sensed data such that a cloud node can only process issued queries over stored data in the encryption domain. For data integrity/completeness, we manipulate a data structure of XOR linked list (X2L), which allows a querist to verify the integrity of retrieved data via the socalled verification information, i.e., neighborhood difference in a storage-efficient manner. We demonstrate the feasibility and efficiency of EQ via experiments conducted on TelosB prototype sensor platform running TinyOS 1.1.15 and comparisons with state-of-the-arts.

We investigate the issue of content authentication for halftone videos transmitted over mobile devices. With an eye to the flickering that is the unique characteristic of halftone video and possesses the property of sparsity, a compressed sensing (CS)-based halftone video authentication method is presented. We show that the restricted isometry property (RIP) in CS can explain the principle of hash matching between two CS-based hashes. Promising results obtained from simulations demonstrate the feasibility of our method.

Image copy detection is an art of searching duplicates from a target database. Computationally efficient and robust detection is still a challenging issue. Inspired by the recent study of sparsity in the context of compressed sensing, we propose a sparse representation-based image copy detection method exploiting sparsity as the cue for searching duplicates. We find that although sparse representation can describe an image in a compact manner, the inherent discriminable features, as far as we know, are not entirely explored. In this paper, we study the discrimination ability inherent in sparsity via online dictionary learning and compact feature descriptor representation. Experimental results show that our method, compared with state-of-the-art, is computationally efficient and attains better or comparable detection performance measured in terms of precision and recall rates.

Scale-space image feature extraction (SSIFE) has been widely adopted in broad areas due to its powerful resilience to attacks. However, the security threat to SSIFE-based applications, which will be addressed in this paper, is relatively unexplored.

Privacy has received considerable attention but is still largely ignored in the multimedia community. Consider a cloud computing scenario where the server is resource-abundant, and is capable of finishing the designated tasks. It is envisioned that secure media applications with privacy preservation will be treated seriously. In view of the fact that scale-invariant feature transform (SIFT) has been widely adopted in various fields, this paper is the first to target the importance of privacy-preserving SIFT (PPSIFT) and to address the problem of secure SIFT feature extraction and representation in the encrypted domain. As all of the operations in SIFT must be moved to the encrypted domain, we propose a privacy-preserving realization of the SIFT method based on homomorphic encryption. We show through the security analysis based on the discrete logarithm problem and RSA that PPSIFT is secure against ciphertext only attack and known plaintext attack. Experimental results obtained from different case studies demonstrate that the proposed homomorphic encryption-based privacy-preserving SIFT performs comparably to the original SIFT and that our method is useful in SIFT-based privacy-preserving applications.

Compressive sensing (CS) has recently attracted much attention due to its unique feature of directly and simultaneously acquiring compressed and encrypted data based on their sparse or compressible properties. To securely transmit compressively sensed multimedia data over networks, it is required to support transcoder to securely convert compressed multimedia into several different types for diverse receivers. In this paper, a secure transcoding scheme for compressive multimedia sensing is proposed. We focus on securely converting compressively sensed multimedia data (not data compressed via standard codec) with a certain number of measurements into other different numbers of measurements without resorting to reconstruct the original data. We show that the security can be achieved via transforming multimedia re-sensing process into another secure domain at the transcoder. We also show that the computational security can be achieved while transmitting compressively sensed data between the sender (or each receiver) and the transcoder over networks.

The two-tier architecture consisting of a small number of resource-abundant storage nodes in the upper tier and a large number of sensors in the lower tier could be promising for large-scale sensor networks in terms of resource efficiency, network capacity, network management complexity, etc. In this architecture, each sensor having multiple sensing capabilities periodically forwards the multidimensional sensed data to the storage node, which responds to the queries, such as range query, top-kquery, and skyline query. Unfortunately, node compromises pose the great challenge of securing the data collection; the sensed data could be leaked to or could be manipulated by the compromised nodes. Furthermore, chunks of the sensed data could be dropped maliciously, resulting in an incomplete query result, which is the most difficult security breach. Here, we propose a simple yet effective hash tree-based framework, under which data confidentiality, query result authenticity, and query result completeness can be guaranteed simultaneously. In addition, the subtree sampling technique, which could be of independent interest to the other applications, is proposed to efficiently identify the compromised nodes. Last, analytical and extensive simulation studies are conducted to evaluate the performance and security of our methods. Prototype implementation on TelosB mote demonstrates the practicality of our proposed methods.

Sensor networks are vulnerable to false data injection attack and path-based denial of service (PDoS) attack. While conventional authentication schemes are insufficient for solving these security conflicts, an en-route filtering scheme, enabling each forwarding node to check the authenticity of the received message, acts as a defense against these two attacks. To construct an efficient en-route filtering scheme, this paper first presents a Constrained Function-based message Authentication (CFA) scheme, which can be thought of as a hash function directly supporting the en-route filtering functionality. Obviously, the crux of the scheme lies on the design of guaranteeing each sensor to have en-route filtering capability. Together with the redundancy property of sensor networks, which means that an event can be simultaneously observed by multiple sensor nodes, the devised CFA scheme is used to construct a CFA-based en-route filtering (CFAEF) scheme. In addition to the resilience against false data injection and PDoS attacks, CFAEF is inherently resilient against false endorsement-based DoS attack. In contrast to most of the existing methods, which rely on complicated security associations among sensor nodes, our design, which directly exploits an en-route filtering hash function, appears to be novel. We examine the CFA and CFAEF schemes from both the theoretical and numerical aspects to demonstrate their efficiency and effectiveness. Moreover, prototype implementation on TelosB mote demonstrates the practicality of our proposed method.

Assessment of image similarity is fundamentally important to numerous multimedia applications. The goal of similarity assessment is to automatically assess the similarities among images in a perceptually consistent manner. In this paper, we interpret the image similarity assessment problem as an information fidelity problem. More specifically, we propose a feature-based approach to quantify the information that is present in a reference image and how much of this information can be extracted from a test image to assess the similarity between the two images. Here, we extract the feature points and their descriptors from an image, followed by learning the dictionary/basis for the descriptors in order to interpret the information present in this image. Then, we formulate the problem of the image similarity assessment in terms of sparse representation. To evaluate the applicability of the proposed feature-based sparse representation for image similarity assessment (FSRISA) technique, we apply FSRISA to three popular applications, namely, image copy detection, retrieval, and recognition by properly formulating them to sparse representation problems. Promising results have been obtained through simulations conducted on several public datasets, including the Stirmark benchmark, Corel-1000, COIL-20, COIL-100, and Caltech-101 datasets.

Scale-invariant feature transform (SIFT) is a powerful tool extensively used in the community of pattern recognition and computer vision. The security issue of SIFT, however, is relatively unexplored. We point out the potential weakness of SIFT, meaning that the SIFT features can be deleted or destroyed while maintaining acceptable visual qualities. To properly achieve the tradeoff between security and robustness of SIFT, we present a cube-based secure transformation mechanism to enable the SIFT method to resist up to the chosen plaintext attack while robustness against geometric attacks can still be maintained. Security analysis and robustness verification are provided to demonstrate the effectiveness of the proposed (and modified) SIFT method.

In this paper, we formulate the problems of image copy detection and image recognition in terms of sparse representation. To achieve robustness, security, and efficient storage of image features, we propose to extract compact local feature descriptors via constructing the basis of the SIFT-based feature vectors extracted from the secure SIFT domain of an image. Image copy detection can be efficiently accomplished based on the sparse representations and reconstruction errors of the features extracted from an image possibly manipulated by signal processing or geometric attacks. For image recognition, we show that the features of a query image can be represented as sparse linear combinations of the features extracted from the training images belonging to the same cluster. Hence, image recognition can also be cast as a sparse representation problem. Then, we formulate our sparse representation problem as an l1-minimization problem. Promising results regarding image copy detection and recognition have been verified, respectively, through the simulations conducted on several content-preserving attacks defined in the Stirmark benchmark and Caltech-101 dataset.

We address an important issue of fully low-cost and low-complex video compression for use in resource-extremely limited sensors/devices. Conventional motion estimation-based video compression or distributed video coding (DVC) techniques all rely on the high-cost mechanism, namely, sensing/sampling and compression are disjointedly performed, resulting in unnecessary consumption of resources. That is, most acquired raw video data will be discarded in the (possibly) complex compression stage. In this paper, we propose a dictionary learning-based distributed compressive video sensing (DCVS) framework to “directly” acquire compressed video data. Embedded in the compressive sensing (CS)-based single-pixel camera architecture, DCVS can compressively sense each video frame in a distributed manner. At DCVS decoder, video reconstruction can be formulated as an l1-minimization problem via solving the sparse coefficients with respect to some basis functions. We investigate adaptive dictionary/basis learning for each frame based on the training samples extracted from previous reconstructed neighboring frames and argue that much better basis can be obtained to represent the frame, compared to fixed basis-based representation and recent popular “CS-based DVC” approaches without relying on dictionary learning.

We address an important issue of fully low-cost and low-complexity video encoding for use in resource limited sensors/devices. Conventional distributed video coding (DVC) does not actually meet this requirement because the acquisition of video sequences still relies on the high-cost mechanism (sampling + compression). Recently, we have proposed a distributed compressive video sensing (DCVS) framework to directly capture compressed video data called measurements, while exploiting correlations among successive frames for video reconstruction at the decoder. The core is to integrate the respective characteristics of DVC and compressive sensing (CS) to achieve CS-based single-pixel camera-compatible video encoder. At DCVS decoder, video reconstruction can be formulated as a convex unconstrained optimization problem via solving the sparse coefficients with respect to some basis functions. Nevertheless, the issue of measurement rate allocation has not been considered yet in the literature. Actually, different measurement rates should be adaptively assigned to different local regions by considering the sparsity of each region for improving reconstructed quality. This paper investigates dynamic measurement rate allocation in block-based DCVS, which can adaptively adjust measurement rates by estimating the sparsity of each block via feedback information. Simulation results have indicated the effectiveness of our scheme. It is worth noting that our goal is to develop a novel fully low-complexity video compression paradigm via the emerging compressive sensing and sparse representation technologies, and provide an alternative scheme adaptive to the environment, where raw video data is not available, instead of competing compression performances against the current compression standards (e.g., H.264/AVC) or DVC schemes which need raw data available for encoding.

As a security primitive, key establishment plays the most crucial role in the design of the security mechanisms. Unfortunately, the resource limitation of sensor nodes poses a great challenge for designing an efficient and effective key establishment scheme for wireless sensor networks (WSNs). In spite of the fact that many elegant and clever solutions have been proposed, no practical key establishment scheme has emerged. In this paper, a ConstrAined Random Perturbation-based pairwise keY establishment (CARPY) scheme and its variant, a CARPY+ scheme, for WSNs, are presented. Compared to all existing schemes which satisfy only some requirements in so-called sensor-key criteria, including (1) resilience to the adversary's intervention, (2) directed and guaranteed key establishment, (3) resilience to network configurations, (4) efficiency, and (5) resilience to dynamic node deployment, the proposed CARPY+ scheme meets all requirements. In particular, to the best of our knowledge, CARPY+ is the first noninteractive key establishment scheme with great resilience to a large number of node compromises designed for WSNs. We examine the CARPY and CARPY+ schemes from both the theoretical and experimental aspects. Our schemes have also been practically implemented on the TelosB compatible mote to evaluate the corresponding performance and overhead.

Low-complexity video encoding has been applicable to several emerging applications. Recently, distributed video coding (DVC) has been proposed to reduce encoding complexity to the order of that for still image encoding. In addition, compressive sensing (CS) has been applicable to directly capture compressed image data efficiently. In this paper, by integrating the respective characteristics of DVC and CS, a distributed compressive video sensing (DCVS) framework is proposed to simultaneously capture and compress video data, where almost all computation burdens can be shifted to the decoder, resulting in a very low-complexity encoder. At the decoder, compressed video can be efficiently reconstructed using the modified GPSR (gradient projection for sparse reconstruction) algorithm. With the assistance of the proposed initialization and stopping criteria for GRSR, derived from statistical dependencies among successive video frames, our modified GPSR algorithm can terminate faster and reconstruct better video quality. The performance of our DCVS method is demonstrated via simulations to outperform three known CS reconstruction algorithms.

Security is still a relatively unexplored issue in image hashing. In this paper, we address this problem and present a new metric, called minimum distortion estimation (MDE), by demonstrating its appropriateness over entropy for secure hashing evaluation. We investigate the relationship between bit changing rate and content distortion via statistical analyses of MDE. This provides a guideline for our design of a new secure image hashing method. The feasibility of our method is further demonstrated via comparisons with two known image hashing methods.

In this paper, a new image hashing scheme satisfying robustness and security is proposed. We exploit the property of dimensionality reduction inherent in compressive sensing/sampling (CS) for image hash design. The gained benefits include (1) the hash size can be kept small and (2) the CS-based hash is computationally secure. We study the use of visual information fidelity (VIF) for hash comparison under Stirmark attacks. We further derive the relationships between the hash of an image and both of its MSE distortion and visual quality measured by VIF, respectively. Hence, based on hash comparisons, both the distortion and visual quality of a query image can be approximately estimated without accessing its original version. We also derive the minimum distortion for manipulating an image to be unauthentic to measure the security of our scheme.

Scale-invariant feature transform (SIFT) is a powerful tool extensively used in the community of pattern recognition and computer vision. However, the security issue of SIFT is relatively unexplored in the literature. This paper investigates the potential weakness of SIFT, meaning that the SIFT features can be deleted or destroyed while maintaining acceptable visual qualities. We then propose an improved scheme to enhance the security of SIFT by introducing a key-based transform process to images. Experimental results demonstrate the effectiveness of our methods.

The major contribution of this paper is to propose a robust en-route filtering scheme for data authentication in sensor networks without relying on unrealistic assumptions.

Wireless visual sensor networks are potentially applicable for several emerging applications. Since the resource-limited restriction for a visual sensor node (VSN), efficient resource allocation for video compression is challenging. In this paper, a power-rate-distortion (PRD) model-based low-complexity multiview video codec is proposed. Our PRD model is used to characterize the relationship between the available resources and the RD performance of our video codec. More specifically, an RD function in terms of the percentages for different coding modes of blocks and the target bit rate under the available resource constraints is derived for optimal coding mode decision. Analytic and simulation results are provided to verify the resource scalability and accuracy of our PRD model in facilitating our low-complexity multiview video codec to achieve good performance.

In this paper, a constrained random perturbation based pairwise keY establishment (CARPY) scheme and its variant, a CARPY+ scheme, for Wireless Sensor Networks (WSNs), are presented. Compared to all existing schemes which satisfy only some requirements in so-called sensor-key criteria, including: 1) resilience to the adversary's intervention, 2) directed and guaranteed key establishment, 3) resilience to network configurations, 4) efficiency, and 5) resilience to dynamic node deployment, the proposed CARPY+ scheme meets all requirements. In particular, to the best of our knowledge, CARPY+ is the first non-interactive key establishment scheme with great resilience to a large number of node compromises designed for WSNs. We examine the CARPY and CARPY+ schemes from both the theoretical and experimental aspects. Our schemes have also been practically implemented on the TelosB compatible mote to evaluate the corresponding performance and overhead.

In this paper, we study the challenging problem of node replication detection. Although defending against node replication attacks demands immediate attention, only a few solutions were proposed. In this paper, an Efficient and Distributed Detection (EDD) scheme and its variant, SEDD, are proposed to resist against node replication attacks in mobile sensor networks. The characteristics possessed by EDD and SEDD include (1) Distributed Detection; (2) Efficiency and Effectiveness; (3) Individual Detection; (4) Network-Wide Revocation Avoidance. Performance comparison with known methods are provided to demonstrate the efficiency of the EDD and SEDD schemes.

This paper presents a constrained function based message authentication (CFA) scheme for wireless sensor networks, which meets all the requirements of the so-called sensor authentication criteria, while most of the existing schemes only achieve partial requirements. In particular, to the best of our knowledge, CFA is the first authentication scheme supporting en-route filtering with only a single packet overhead. We examine the CFA scheme from both the theoretical and experimental aspects. Our method has also been practically implemented on the TelosB compatible mote for performance evaluation.

Video halftoning is a key technology for use in the innovative display - electronic paper (e-paper). Since e-paper is power-limited, halftone video compression becomes an emerging issue but is still relatively unexplored. In this paper, this issue is addressed and a novel halftone video compression scheme is proposed. Our scheme is mainly composed of three components: block decomposition, block-based halftone quantization, and source coding. We evaluate the proposed method via lossless halftone video compression comparison with the famous standard, JBIG2. In addition, we demonstrate the rate-distortion performance of the proposed lossy halftone video compression method.

Multicast video authentication is important to verify the authenticity of video data transmitted over the network. In this paper, we focus on unequal authenticity protection for video multicasting. A two-stage authentication strategy, which is composed of hash-based authentication and FEC-based correction, is proposed to resist the pollution attack. Different from the traditional methods, the FEC parameters instead of the rate are derived via a RD optimization procedure. In addition, we also analyze the issues of security strength, authentication rate, and hash length for the proposed method. The analyses show that our method with unequal hash length indeed achieves better RD performance. Simulation results further demonstrate the effectiveness of the proposed method.

Video halftoning is a key technology for use in the new display device, electronic paper (e-paper). One challenging issue is how to save the limited power of mobile e-paper device when a halftone video is displayed with various frame rates. In this paper, we propose a power-scalable multi-layer halftone video display scheme, which is composed of layer coding, non-uniform sampling, and flicker rate reduction. Our method not only efficiently save power over the state of the art video halftoning technology but also keep the quality of halftone video nearly unchanged when power saving is additionally considered. Experimental results demonstrate the effectiveness of the proposed method.

This paper presents a Constrained Random Perturbation Vector-based (CRPV) pairwise key establishment scheme and its variant, CRPV+ scheme, for wireless sensor networks (WSNs). Compared to all existing schemes which satisfy only some requirements in a so-called versatileness criteria, the CRPV+ scheme meets all requirements. In particular, the performance improvement of our schemes does not rely on tradeoffs among different requirements, but comes from the use of our constrained random vector strategy.

By launching the node replication attack, the adversary can place the replicas of captured sensor nodes back into the sensor networks in order to eavesdrop the transmitted messages or compromise the functionality of the network. Although defending against node replication attacks demands immediate attention, only a few solutions were proposed. Most of the existing distributed protocols adopt the witness finding strategy, which selects a set of sensor nodes somewhere as the witnesses, to detect the replicas. However, the energy consumption of the witness finding strategy is remarkably high and even gets worse in mobile networks. In addition, the location information is necessary for each node if the witness finding strategy is applied. In this paper, a novel protocol, called extremely Efficient Detection (XED), is proposed to resist against node replication attacks in mobile sensor networks. The advantages of XED include (1) only constant communication cost is required for replica detection; (2) the location information of sensor nodes is not required. Performance analyses and comparison with known methods are provided to demonstrate the effectiveness of our protocol.

To meet the requirements of resource-limited video sensors, low-complexity video encoding technique is highly desired. In this paper, a low-complexity multi-view distributed video encoding scheme by using the correlations among video frames from adjacent video sensor nodes (VSNs) via robust media hashing at encoder and the global motion parameters estimated and fed back from the decoder is proposed. The frames from adjacent VSNs are warped into the same view-direction based on the global motion parameters. Then, the significant differences between the warped key frame and the non-key frame from adjacent VSNs are efficiently extracted based on robust media hashing for non-key frame compression. The key is that few data (hash information) exchanges among adjacent VSNs are allowed to efficiently exploit the correlations among VSNs. The coding performance and energy consumption of the proposed encoder have been verified through simulations and comparisons with existing low-complexity video encoders.

Video halftoning is a key technology for use in the new display device, electronic paper (e-paper). This is still a rather unexplored field. The challenging issue of video halftoning is the elimination of flicker flaw that will appear due to error diffusion in the temporal domain. In this paper, we propose a new video halftoning method, which is composed of spatial error diffusion and inter-frame reference error diffusion. In addition, since our method can efficiently reduce the flicker flaws, another advantage is that the halftone video sequence can be efficiently compressed. When compared with traditional 2D and 3D error diffusion techniques, experimental results show that our method can significantly reduce the average flicker rates.

In this paper, a new multimedia joint fingerprinting and encryption (JFE) scheme embedded into the advanced access content system (AACS) is proposed. Like other security-related systems, there exist some security threats to the proposed framework. To cope with these difficulties, the contributions of this paper include: (i) we apply multimedia encryption at different points to resist some attacks points; and (ii) we propose rewritable fingerprint embedding (RFE) to deal with some multi-point collusion attacks. Experimental results are provided to demonstrate the proposed AACS-compatible JFE method.

Measurement of end-to-end available bandwidth has received considerable attention due to its potential use in improving QoS. Available bandwidth enables the sending rate to adapt to network conditions, so that packet loss, caused by congestion, can be significantly reduced before error control mechanisms are finally employed. To this end, we propose a probing noise resilient available bandwidth estimation scheme, called JitterPath, which is adaptive to both the fluid and bursty traffic models. Two key factors, one-way delay jitter and accumulated queuing delay, are both exploited to predict the type of queuing region for each packet pair. Then, the bottleneck utilization information included in the joint queuing regions is estimated and used to quantify the captured traffic ratio, which indicates the relationship between the probing rate and available bandwidth. The contributions of our method are as follows: 1) JitterPath can work without being restricted to fluid traffic models; 2) since JitterPath does not directly use the bottleneck link capacity to calculate the available bandwidth, it is feasible for use in a multihop environment with a single bottleneck; and 3) JitterPath inherently reduces the impact of probing noises under the bursty cross traffic model. Extensive simulations, Internet experiments, and comparisons with other methods were conducted to verify the effectiveness of our method under both single-hop and multihop environments

Media authentication aims to judge the integrity of media content in the sense that malicious tampering should be detected while incidental modifications should be tolerated. This study investigates the resistance of a semi-fragile watermarking method to incidental manipulations with focus on resisting compressions with lower bit rates. To this end, we develop an informed authentication watermarking scheme based on reconstructing transformed-domain data in the sense that the effect resulted from incidental modifications can be eliminated through reconstruction. Statistical analyses and experimental results are provided to validate the proposed method.

In distributed video coding, individual frames are encoded independently but decoded conditionally. The Wyner-Ziv theorem-based source coding with side information only available at the decoder states that an intraframe encoder with interframe decoder system can approach the efficiency of a conventional interframe encoder and decoder system. In this paper, a new block discrete cosine transform (DCT)-based Wyner-Ziv video codec with coding mode-aided motion compensation at the decoder is proposed. The key is that for each block, a large amount of candidate blocks are evaluated based on some criteria derived from Reed-Solomon (RS) decoding and best neighborhood matching to find the best candidate block as the side information. Another characteristic is that error correction code (ECC) decoding is proposed to participate in generating side information. Compared with some known Wyner-Ziv video coding systems, in the proposed video codec, no extra information should be transmitted and feedback channel is unnecessary. The coding performance of our method has been verified through simulations.

To meet the requirement of distributed video coding in resource-limited sensor networks, Wyner-Ziv theorem-based source coding with side information available at the decoder states that an intraframe encoder with interframe decoder system can achieve comparable coding efficiency of a conventional video codec. Most existing Wyner-Ziv video coding systems are with light encoder and heavy decoder. In this paper, a new content-aware media hash-based Wyner-Ziv video codec with light encoder and light decoder is proposed. The key is that the significant differences between a video frame and its reference frame are efficiently extracted and used for frame recovery based on robust image hashing without needing to perform motion estimation. The particular contribution of our method is its low complexity in both the encoder and decoder sides. Simulation results demonstrate the achievable coding efficiency of our method in particular for videos with small and middle motions

We propose a one-way delay jitter based scheme, "jitterpath," for available bandwidth estimation. Common assumptions, including use of the fluid traffic model and use of the bottleneck link capacity, that have been made in the literature are relaxed in this study. We exploit one-way delay jitter and accumulated queuing delay to predict the type of a queuing region for each packet pair. In addition, we quantify the captured traffic ratio, which is defined as the total output gaps of joint queuing regions per total input gaps, and use it to derive the relationship between probing rate and available bandwidth. We further investigate how the estimation resolution and the probing noise ratio are related to the accuracy of available bandwidth estimation. Extensive simulations and real-network experiment have been conducted and comparisons with other methods have been made to verify the effectiveness of our method, no matter whether single-hop or multi-hop environments are considered

The major disadvantage of existing watermarking methods is their limited resistance to extensive geometric attacks. In addition, we have found that the weakness of multiple watermark embedding methods that were initially designed to resist geometric attacks is their inability to withstand the watermark-estimation attacks (WEAs), leading to reduce resistance to geometric attacks. In view of these facts, this paper proposes a robust image watermarking scheme that can withstand geometric distortions and WEAs simultaneously. Our scheme is mainly composed of three components: 1) robust mesh generation and mesh-based watermarking to resist geometric distortions; 2) construction of media hash-based content-dependent watermark to resist WEAs; and 3) a mechanism of false positive-oriented watermark detection, which can be used to determine the existence of a watermark so as to achieve a tradeoff between correct detection and false detection. Furthermore, extensive experimental results obtained using the standard benchmark (i.e., Stirmark) and WEAs, and comparisons with relevant watermarking methods confirm the excellent performance of our method in improving robustness. To our knowledge, such a thorough evaluation has not been reported in the literature before.

The existing halftone image watermarking methods were proposed to embed a watermark bit in a halftone dot, which corresponds to a pixel, to generate stego halftone image. This one-to-one mapping, however, is not consistent with the one-to-many strategy that is used by current high-resolution devices, such as computer printers and screens, where one pixel is first expanded into many dots and then a halftoning processing is employed to generate a halftone image. Furthermore, electronic paper or smart paper that produces high-resolution digital files cannot be protected by the traditional halftone watermarking methods. In view of these facts, we present a high-resolution halftone watermarking scheme to deal with the aforementioned problems. The characteristics of our scheme include: (i) a high-resolution halftoning process that employs a one-to-many mapping strategy is proposed; (ii) a many-to-one inverse halftoning process is proposed to generate gray-scale images of good quality; and (iii) halftone image watermarking can be directly conducted on gray-scale instead of halftone images to achieve better robustness.

A common way of resisting protocol attacks is to employ cryptographic techniques so that provable security can be retained. However, some desired requirements of watermarking such as blind detection and robustness are lost. This paper studies the issue of security against protocol attacks based on a mesh-based media hash-dependent image watermarking approach while maintaining the aforementioned requirements. Our main contributions include (1) media hashing instead of cryptographic hashing is used so that blind detection is still satisfied; (2) robustness against signal processing attacks is retained; (3) the difficulty of resisting ambiguity attack is derived to be equivalent to that of resisting challenging geometric attacks including cropping with larger parts discarded and rotation with larger degrees so that an acceptable trade-off between false positive and false negative can be achieved.

Designing a powerful fragile watermarking technique for authenticating three-dimensional (3-D) polygonal meshes is a very difficult task. Yeo and Yeung were first to propose a fragile watermarking method to perform authentication of 3-D polygonal meshes. Although their method can authenticate the integrity of 3-D polygonal meshes, it cannot be used for localization of changes. In addition, it is unable to distinguish malicious attacks from incidental data processings. In this paper, we trade off the causality problem in Yeo and Yeung's method for a new fragile watermarking scheme. The proposed scheme can not only achieve localization of malicious modifications in visual inspection, but also is immune to certain incidental data processings (such as quantization of vertex coordinates and vertex reordering). During the process of watermark embedding, a local mesh parameterization approach is employed to perturb the coordinates of invalid vertices while cautiously maintaining the visual appearance of the original model. Since the proposed embedding method is independent of the order of vertices, the hidden watermark is immune to some attacks, such as vertex reordering. In addition, the proposed method can be used to perform region-based tampering detection. The experimental results have shown that the proposed fragile watermarking scheme is indeed powerful.

Video transmission needs a stable sending rate in order that the video can be displayed showing uniform quality. In addition, a lower packet loss rate is rather helpful in reducing video quality degradation. Therefore, reliable available bandwidth estimation becomes an indispensable step towards robust transmission of multimedia data. The existing available bandwidth estimation methods cannot deal with the false estimation problem and thereby precise estimation is impossible. In this paper, we propose a reliable available bandwidth estimation method, based on distinguishing queuing regions and resolving false estimations. Promising simulation results indicate that our method can obtain the available bandwidth precisely, no matter what the network environment is; single-bottleneck or multiple-bottleneck.

One of the key challenges for a watermarking scheme to be mandated in a digital right management (DRM) system is the robustness. This paper is focused on exploring the robustness against the watermark-estimation attacks (WEAs) that are clever at disclosing hidden information for unauthorized purposes without sacrificing media's quality. In WEAs, the collusion attack naturally occurs in video watermarking while the copy attack adapts to any media watermarking. In view of this, the aim of this study is to deal with the WEAs by means of a video frame-dependent watermark (VFDW). We begin by gaining insight into the WEAs, leading to formal definitions of "optimal watermark prediction" and "perfect cover data recovery". Subject to these definitions, the video-frame hash is addressed as a constituent component of the VFDW for antiestimation of hidden watermarks. Both mathematical analyses and experiment results consistently verify the antidisclosure capability of the video content-dependent watermarking scheme. Our approach is the first work that takes resistance to both the collusion and copy attacks into consideration.

The paper considers especially attacks that can disclose or counterfeit hidden information. We first explore copy and collage attacks, and find that they can be specified as the generalized copy attack (GCA). Then, we propose to embed a single type of watermark to achieve the multiple purposes of content protection and authentication. Our scheme mainly relies on the deployment of content-dependent watermarks (CDWs), where each is a combination of an informative watermark and a robust hash. Mathematical analyses and experimental results consistently verify the effectiveness of the proposed scheme.

Due to the desired non-invasive property, non-data hiding (called media hashing here) is considered to be an alternative to achieve many applications previously accomplished with watermarking. Recently, media hashing techniques for content identification have been gradually emerging. However, none of them are really resistant against geometrical attacks. In this paper, our aim is to propose a geometry-invariant image hashing scheme, which can be employed for content copy detection and tracing. Our system is mainly composed of three components: (i) robust mesh extraction; (iii) mesh-based robust hash extraction; and (iii) hash matching for similarity measurement. Exhaustive experimental results obtained from benchmark attacks have confirmed the performance of the proposed method.

This paper proposes a significant motion vector protection (SMVP) scheme for error-resilient transmission of videos. In terms of a rate-distortion optimization model, we show how to determine the significant motion vectors (SMVs) and how much rate space should be preserved to store SMVs. The idea behind our method is to give more protection to significant data. As a result, our method can be regarded as a kind of unequal error protection mechanisms. In comparison with the conventional forward error correction (FEC) and error concealment methods, experimental results demonstrate the effectiveness of the proposed method. In particularly, our method shows its superiority under the situation of higher packet loss rates.

In this paper, an image steganographic method with security level confined to a dual security criterion is addressed. Dual security is designed to be measured by kurtosis in the spatial domain and by relative entropy in the frequency domain, simultaneously. Messages are encoded into the wavelet domain by adjusting the magnitude (least few significant bits) relationship between four neighboring child nodes (called permutations of order representations) to satisfy the dual security criterion. Relationship between security, perceptual fidelity, and capacity are investigated on various images.

In this paper, we propose an iterated two-band filtering method to solve the selective image smoothing problem. We prove that a discrete computation step in an iterated nonlinear diffusion-based filtering algorithm is equivalent to a sequence of operations, including decomposition, regularization, and then reconstruction, in the proposed two-band filtering scheme. To correctly separate the high frequency components from the low frequency ones in the decomposition process, we adopt a dyadic wavelet-based approximation scheme. In the regularization process, we use a diffusivity function as a guide to retain useful data and suppress noises. Finally, the signal of the next stage, which is a "smoother" version of the signal at the previous stage, can be computed by reconstructing the decomposed low frequency component and the regularized high frequency component. Based on the proposed scheme, the smoothing operation can be applied to the correct targets. Experimental results show that our new approach is really efficient in noise removing.

The existing digital data verification methods are able to detect regions that have been tampered with, but are too fragile to resist incidental manipulations. This paper proposes a new digital signature scheme which makes use of an image's contents (in the wavelet transform domain) to construct a structural digital signature (SDS) for image authentication. The characteristic of the SDS is that it can tolerate content-preserving modifications while detecting content-changing modifications. Many incidental manipulations, which were detected as malicious modifications in the previous digital signature verification or fragile watermarking schemes, can be bypassed in the proposed scheme. Performance analysis is conducted and experimental results show that the new scheme is indeed superb for image authentication.

Watermarking with both oblivious detection and high robustness capabilities is still a challenging problem. In this paper, we tackle the aforementioned problem. One easy way to achieve blind detection is to use denoising for filtering out the hidden watermark, which can be utilized to create either a false positive (copy attack) or false negative (denoising and remodulation attack). Our basic design methodology is to exploit prior knowledge available at the detector side and then use it to design a "nonblind" embedder. We prove that the proposed scheme can resist two famous watermark estimation-based attacks, which have successfully cracked many existing watermarking schemes. False negative and false positive analyses are conducted to verify the performance of our scheme. The experimental results show that the new method is indeed powerful.

Video object (VO) is a very important concept in the MPEG-4 standard. Video objects may be purposely cut and pasted for illegal use. A robust watermarking scheme for video object protection is proposed. For each segmented video object, a watermark is embedded by a new technology whose design is based on the concept of communications with side information. To solve the asynchronous problem caused by object placement, we propose to use eigenvectors of a video object for synchronization of rotation and flipping. Preliminary results have demonstrated the robustness of the proposed method.

A noise-type Gaussian sequence is most commonly used as a watermark to claim ownership of media data. However, only a 1 bit information payload is carried in this type of watermark. For a logo-type watermark, the situation is better because it is visually recognizable and more information can be carried. However, since the sizes and shapes of logos for different organizations are different, the flexibility of use of a logo-type watermark will certainly be degraded. We design a more flexible type of watermark, i.e., a message. Since a message is composed of a finite number of ASCII-type characters, it is by nature vulnerable to attacks. Therefore, we propose to choose a set of nonlinear Hadamard codes that has the maximum Hamming distance between any two constituent codes to replace the original ASCII-type inputs. This design will make our system much more fault-tolerant in comparison with ASCII-code based systems under direct attack. To recover an attacked Hadamard code, we use a trained backpropagation neural network to perform inexact matching. Experimental results demonstrate that our message-based cocktail watermarking system is superb in terms of robustness and flexibility.

Watermarking with both oblivious detection and high robustness capabilities is still a challenging problem for copyright protection up to now. In order to tackle the above mentioned problem we propose to exploit prior knowledge available at the watermark detector side to design a "non-blind" embedder. We prove that the proposed scheme can resist two famous denoising-based attacks, which have successfully cracked many existing watermarking schemes.

We propose a novel multipurpose watermarking scheme, in which robust and fragile watermarks are simultaneously embedded, for copyright protection and content authentication. By quantizing a host image's wavelet coefficients as masking threshold units (MTUs), two complementary watermarks are embedded using cocktail watermarking and they can be blindly extracted without access to the host image. For the purpose of image protection, the new scheme guarantees that, no matter what kind of attack is encountered, at least one watermark can survive well. On the other hand, for the purpose of image authentication, our approach can locate the part of the image that has been tampered with and tolerate some incidental processes that have been executed. Experimental results show that the performance of our multipurpose watermarking scheme is indeed superb in terms of robustness and fragility.

Watermarking with oblivious detection and high robustness capabilities together is still a challenging problem up to now. The existing methods are either robust or oblivious but it is difficult to achieve both goals simultaneously. In this paper, oblivious detection is formulated as a blind source separation problem by regarding the hidden watermarks as noise. To keep high robustness, our non-oblivious cocktail watermarking scheme, which is very robust, is adopted to combine with the oblivious detection mechanism. The proposed oblivious cocktail watermarking can be applied to global watermarking and regional watermarking. Experimental results have demonstrated the powerfulness of our method.

We proposed a new dyadic wavelet-based conduction approach to take the place of the nonlinear diffusion equation for selective image smoothing. We also proved that the proposed iterated system always satisfies the so-called maximum-minimum principle no matter what kind of wavelet basis is used. Since the proposed approach does not require one to solve a partial differential equation (PDE), it is therefore more efficient and accurate than the conventional nonlinear diffusion/conduction-based methods. Experimental results using 1-D synthetic data and a real image demonstrated that the proposed method can efficiently remove noise and preserve real data.

The objective of this paper is to propose an image authentication scheme, which is able to detect malicious tampering of images even they have also been incidentally distorted. By modeling incidental and malicious distortions as Gaussian distributions with small and large variances, respectively, we propose to embed a watermark in the wavelet domain by a mean quantization technique. Due to the various probabilities of tamper response at each scale, these responses are integrated to make a decision on the tampered areas. Statistical analysis is conducted and experimental results are given to demonstrate that our watermarking scheme is able to detect malicious attacks while tolerating incidental distortions.

An audio protection and authentication scheme is proposed. By quantizing a host audio's FFT-coefficients as masking threshold units (MTUs), two complementary watermarks are designed and embedded using our cocktail watermarking method. For audio protection, high robustness can be achieved; whereas for audio authentication, tampered regions can be detected. Both of the above mentioned goals are accomplished in an oblivious manner. Experimental results indicate that our multipurpose audio watermarking scheme is remarkably effective.

A novel image protection scheme called "cocktail watermarking" is proposed in this paper. We analyze and point out the inadequacy of the modulation techniques commonly used in ordinary spread spectrum watermarking methods and the visual model-based ones. To resolve the inadequacy, two watermarks which play complementary roles are simultaneously embedded into a host image. We also conduct a statistical analysis to derive the lower bound of the worst likelihood that the better watermark (out of the two) can be extracted. With this "high" lower bound, it is ensured that a "better" extracted watermark is always obtained. From extensive experiments, results indicate that our cocktail watermarking scheme is remarkably effective in resisting various attacks, including combined ones.

An efficient texture representation for unsupervised segmentation is addressed based on the concept of Wold decomposition. Textures are described by the wavelet tuned to various scales and rotations to describe its deterministic component, and by the autoregressive model to describe its indeterministic component. The wavelet features and the AR parameters capturing the perceptual properties, "periodicity", "directionality", and "randomness", respectively, have been proved to be consistent with human texture perception. The performance of our approach is demonstrated on Brodatz textures and natural textured images.

In this correspondence, a method is proposed for estimating the surface orientation of a planar texture under perspective projection based on the ridge of a two-dimensional (2-D) continuous wavelet transform (CWT). We show that an analytical solution of the surface orientation can be derived from the scales of the ridge surface. A comparative study with an existing method is given.

A common assumption of the shape from texture problem is that a perceived image mainly contains only one type of texture with the same surface orientation. Unfortunately, a natural image is often composed of more than one textures. In order to solve the shape from texture problem in a practical manner, we need to segment 3D textured images. In this paper, we propose a new algorithm for the task. We estimate the local surface orientations from the the scales of the ridge points of continuous wavelet transform. Then, the local surface orientations are used as the features for texture segmentation. Textured images synthesized from Brodatz's album and several natural images demonstrate the performance of our method.

We propose a new shape from texture method based on the ridge of continuous wavelet transform. This method determines the orientations of a planar surface in a direct way under the perspective projection model. The variations of the image projected from a planar surface can be accurately characterized by the ridge of the continuous wavelet transform. The ridge of the 1-D signal and 2-D image are represented as a ridge curve and ridge plane, respectively. Ridges represent the energy concentration in the time-frequency plane where the energy is a local maxima. We show that the ridge of the projected image is a parabolic plane with a rotation angle equal to the tilt angle of the planar surface. The ridge is then rotated with the angle such that the slant effect appears in the X-axis and plays no role along the Y-axis. As a result, the rotated ridge plane can be regarded as the plane composed of many 1-D ridge curves. The slant angle of the 2-D image is thus obtained from the derived slant angle of the 1-D signal. A voting method and a curve fitting method are developed to obtain the slant angle of the 1-D signal. Several synthetic and real-world images have demonstrated the robustness and accuracy of our method.