Person re-identification (ReID) is a fundamental task in many real-world applications such as pedestrian trajectory tracking. However, advanced deep learning-based ReID models are highly susceptible to adversarial attacks, where imperceptible perturbations to pedestrian images can cause entirely incorrect predictions, posing significant security threats. Although numerous adversarial defense strategies have been proposed for classification tasks, their extension to metric learning tasks such as person ReID remains relatively unexplored. Moreover, the several existing defenses for person ReID fail to address the inherent unique challenges of adversarially robust ReID. In this paper, we systematically identify the challenges of adversarial defense in person ReID into two key issues: model bias and composite generalization requirements. To address them, we propose a debiased dual-invariant defense framework composed of two main phases. In the data balancing phase, we mitigate model bias using a diffusion-model-based data resampling strategy that promotes fairness and diversity in training data. In the bi-adversarial self-meta defense phase, we introduce a novel metric adversarial training approach incorporating farthest negative extension softening to overcome the robustness degradation caused by the absence of classifier. Additionally, we introduce an adversarially-enhanced self-meta mechanism to achieve dual-generalization for both unseen identities and unseen attack types. Experiments demonstrate that our method significantly outperforms existing state-of-the-art defenses.

Ensuring data confidentiality while achieving efficient deduplication is critical for cloud storage services. Existing works on encrypted data deduplication operate under a deduplication-before-storage (DbS) model, identifying duplicates only during the data uploading phase. They require the owners of duplicate data or additional key servers to assist in the data uploading phase for enabling encrypted deduplication. This makes it hard to balance data security and deduplication effectiveness. This paper introduces a new paradigm named deduplication-while-storage (DwS), which differs from the existing DbS model by enabling dynamic deduplication throughout the entire storage lifecycle, activated whenever owners of potential duplicate data are available. This paradigm aims to significantly boost deduplication ratio without compromising the security of outsourced data. To instantiate the DwS paradigm, we develop SGX-DwS, a shielded storage system that utilizes Intel SGX to support encrypted data deduplication. SGX-DwS builds on an ownership-based encryption mechanism that enables secure equality checks on ciphertexts with the cooperation of owners of duplicate data. It adopts a two-phase deduplication strategy. The first phase processes duplicates during uploading if a previous uploader of duplicate data is online and willing to assist (with incentives provided for participation), while the second phase addresses remaining duplicates during the storage phase when owners of the potential duplicates are simultaneously online and cooperative. Our experimental results demonstrate that SGX-DwS achieves high deduplication ratio with modest overhead, even in large-scale cloud storage environments. Specifically, the improvement in deduplication ratio achieved by SGX-DwS is up to 92.49% as compared to that in the DbS model.

Cloud services have been commonly leveraged to store and manage the exponential growth of images, yet this also comes with critical data privacy concerns. Reversible data hiding over encrypted images (RDH-EI) techniques can embed data into encrypted images and support lossless recovery, which can provide an effective solution for securely managing private images in the cloud. However, existing schemes generally suffer from low embedding capacity. Moreover, most of them rely on a single cloud server, which introduces a single point of failure. In this paper, we first propose a pixel correlation recovery (PCR) technique for restoring the pixel correlation excessively disrupted during encryption. Using the PCR technique, we develop a secure (r, n)-threshold RDH-EI scheme with large embedding capacity and avoidance of single point of failure. In our scheme, a content owner encrypts a confidential image into n shares and distributes them across n independent cloud servers. We design a new encoding method enabling each cloud server to efficiently encode the share, preserving capacity for data embedding. An authorized receiver can later extract the embedded data and reconstruct the confidential image from r shares. Experiments demonstrate that our scheme achieves significantly larger embedding capacity over state-of-the-art schemes.

Vertical federated learning (VFL) enables collaborative learning across different entities with disjoint data features for the same instances. In VFL, passive parties with partial data features extract embeddings from their data and forward them to the active party, which holds disjoint data features and labels, for aggregation and subsequent prediction. However, some passive parties may act as free-riders and submit valueless embeddings to deceive rewards, which undermines the fairness of collaborative training and increases communication overhead. Compared to horizontal federated learning (HFL), detecting free-riders in VFL is more challenging due to the distinct data features and heterogeneous embeddings each party produces. This makes it difficult to identify disguised embeddings of free-riders using anomaly detection methods typically employed in HFL. This paper proposes the first free-rider detection strategy in VFL using an unsupervised auxiliary task based on maximum mean discrepancy (MMD). It helps benign parties capture shared information from the active party, resulting in smaller MMD distances for benign embeddings compared to those of free-riders. Additionally, considering that embeddings may be exploited to infer local data, we introduce PVF-FD, a ciphertext-domain verifiable embedding learning scheme that enables the main and auxiliary tasks to be performed simultaneously in a privacy-preserving manner. We formally analyze the security of PVF-FD. Experimental results demonstrate that PVF-FD can effectively detect free-riders, reduce communication overhead, and maintain the performance of the main task.

With advancements in the computer industry, 3D objects are now widely used in various applications, including game development, animation production, and industrial design. This growing adoption has increased the need for effective content security and copyright protection for 3D objects. However, existing encryption and watermarking techniques often operate independently, leading to low efficiency and weak coupling between security and copyright protection. To address these gaps, this paper presents a novel method that integrates watermark embedding into the 3D object decryption process, simultaneously ensuring content security and copyright protection. Specifically, a Look-Up Table (LUT)-based encryption method is employed to secure 3D object data, while a Spread Transform Dither Modulation (ST-DM)-based watermarking method is used to embed user-specific identity information during decryption. Unlike conventional approaches that apply encryption and watermarking separately, the proposed method enables efficient 3D object sharing, as the owner only needs to encrypt the object once, regardless of the number of authorized users. The encrypted model can then be distributed securely via multicast and caching. Decryption with personalized keys produces distinct watermarked 3D objects, allowing for reliable traceability of unauthorized redistribution. Experimental results and theoretical evaluations demonstrate that the proposed method delivers satisfactory visual quality, efficiency, robustness, and security.

Federated learning (FL) is a compelling privacy-friendly paradigm that allows multiple clients to jointly train a model by sharing only gradient updates instead of their local datasets. Since gradient updates may still expose sensitive information, a line of research has explored the use of local differential privacy (LDP) mechanisms to formally safeguard these updates. Under LDP, each client perturbs its gradients locally prior to sharing. However, LDP often leads to a significant degradation in model utility due to the addition of large noises. To enable a better balance between privacy and utility, an increasing trend is to leverage the shuffle model of differential privacy (DP) in FL, which introduces an intermediate shuffling operation on the perturbed gradients, enabling privacy amplification. Following this trend, we present${\sf Camel}$, a communication-efficient and maliciously secure FL framework operating under the shuffle model of DP. A key difference of${\sf Camel}$from existing works is its new support for integrity checks on the shuffle computation, providing security against a malicious adversary. To achieve this,${\sf Camel}$builds on a trending cryptographic technique called secret-shared shuffle, and augments it by our custom methods for system-wide communication optimization and lightweight server-side integrity verification. Furthermore, we provide a formal analysis of privacy loss by employing Rényi differential privacy (RDP) for the entire FL process, which allows a tighter privacy bound. Our comprehensive experimental results show that${\sf Camel}$outperforms current state-of-the-art approaches in achieving better privacy-utility trade-offs, while maintaining promising performance.

With the rapid advancement of deep learning, text-to-speech and voice conversion systems can now generate synthetic speech, commonly known as deepfake speech, that sounds nearly identical to real human voices. This raises serious threats to public security, driving the urgent need for reliable detection methods. However, most existing approaches rely only on either raw waveform or spectrogram features, overlooking cross-view relationships that could reveal artifacts from unknown spoofing attacks. To address this gap, we propose a tri-view collaborative graph learning framework to enhance detection robustness. Our model integrates three complementary views: 1D waveform features, 2D spectral features, and linguistically derived features from an automatic speech recognition (ASR) system. To improve both discriminability and cross-view independence, we design a Tri-View Contrastive Learning (TVCL) framework, which employs cross-view contrastive loss to emphasize cross-view complementarity and intra-view contrastive loss to strengthen class separation within each view. We further introduce a Dynamic Graph Attention Network (DGAT) that captures temporal dependencies across the multi-view features. Through attention-based aggregation and a learnable weighting mechanism, the DGAT adaptively balances contributions from different views, suppressing noise and promoting complementary cooperation. Finally, the graph-level embeddings produced by the DGAT are used for classification. Extensive experiments on five benchmark datasets demonstrate the superiority of our approach, showing consistent improvements over state-of-the-art methods in cross-method, cross-dataset, and cross-language scenarios.

Discrete chaotic maps in the real number field have been widely investigated and applied to various applications. However, there has been limited focus on constructing discrete chaotic maps with complicated dynamics in the complex field. In light of this, this article proposes a 1-D complex-variable chaotic model (1-D-CCM), which can produce a multitude of 1-D complex-variable chaotic maps by combining unbounded analytic functions and locally bounded analytic functions. To illustrate the effectiveness of 1-D-CCM, we construct two 1-D complex-variable chaotic maps by combining inverse trigonometric functions and hyperbolic trigonometric functions. We provide theoretical proof of a new 1-D complex-variable chaotic map as one example to demonstrate that the generated chaotic maps satisfy the chaos definition in terms of Lyapunov exponent. Property analysis reveals distinct strange attractors and hyperchaotic behaviors for the two 1-D complex-variable chaotic maps. Performance evaluations show that the example maps of 1-D-CCM model can achieve a 0–1 test value of 1.0017, a$C_{0}$complexity of 0.7253, a correlation dimension of 2.0242, and a sample entropy of 0.8288. Experimental results demonstrate superior performance indicators compared to other representative chaotic maps. We construct a hardware platform using a microcontroller to implement the attractors of the two new complex-variable chaotic maps. Finally, we design pseudorandom number generators to demonstrate the potential applications of the two 1-D complex-variable chaotic maps.

Light Field (LF) images provide rich visual representations of 3D scenes by capturing both spatial and angular information of light rays. However, their high dimensions present substantial challenges for conventional 2D image watermarking techniques in effectively ensuring copyright protection. In this work, we propose a deep learning-based Spatial-Angular Consistency waterMarking (SACMark) network, designed to address the unique challenges of watermark embedding and extraction in LF images. SACMark employs a spatial-angular feature extraction module to capture the multidimensional information of LF images and introduces consistency matching and fusion strategies to enhance feature utilization. The network adopts an encoder-noise-decoder architecture, optimized through adversarial training to improve the imperceptibility and robustness of the watermark. Experimental results demonstrate that SACMark maintains high visual quality across various embedding capacities and has minimal impact on depth estimation. Compared to traditional LF watermarking approaches and existing deep learning-based methods for 2D images, SACMark demonstrates improved resilience to noise while preserving essential LF characteristics. These findings suggest that SACMark holds promise for practical applications and may contribute to future developments in secure and adaptive LF image protection.

Reversible data hiding over encrypted images (RDH-EI) is an important technique for secure cloud image management but existing schemes often exhibit high computational complexity, low embedding rates, and excessive data expansion. This article addresses these issues by analyzing block-based secret sharing, revealing significant intra-block data redundancy. Based on this observation, we propose two space-preserving methods: the direct space-vacating method and the image-shrinking-based space-vacating method. Using these techniques, we design two novel RDH-EI schemes: a high-capacity RDH-EI scheme and a size-reduced RDH-EI scheme. The high-capacity RDH-EI scheme directly creates embedding space in encrypted images, eliminating the need for complex space-vacating operations and achieving higher and more stable embedding rates. In contrast, the size-reduced RDH-EI scheme minimizes data expansion by discarding unnecessary shares, resulting in smaller encrypted images. Experimental results show that the high-capacity RDH-EI scheme outperforms existing methods in terms of embedding capacity, while the size-reduced RDH-EI scheme achieves strong performance in minimizing data expansion. Both schemes offer effective solutions for RDH-EI challenges.

The field of privacy-preserving computation has recently seen a surge in specialized methods for Private Set Intersection (PSI) and Private Set Union (PSU). The primary focus of existing research lies in the multi-party setting, where set owners collaboratively execute PSI/PSU protocols on their sets. Limited research has investigated the more scalable outsourced service setting, where set owners secretly share their sets among a set of servers that collaboratively provide PSI/PSU query services over the secret-shared data. In this paper, we present FastPSC, a new system design supporting maliciously secure PSI/PSU in the outsourced service setting. FastPSC delicately bridges lightweight secure computation techniques and differential privacy mechanisms. The key insight is to leverage differentially private leakage to achieve a significant efficiency boost in secure and accurate intersection and union query services. Experiments show that with differentially private leakage allowed, FastPSC can achieve a significant performance advantage over the-state-of-the-art prior works without differentially private leakage. Specifically, compared with the work by Mohasselet al.(CCS'20) with semi-honest security, FastPSC achieves a$1.5\times$–$52.2\times$speedup and reduces server-side communication cost by 78%–98%. Compared with the work by Asharovet al.(CCS'23) with malicious security, FastPSC achieves a$4.2\times$–$7.4\times$speedup and reduces server-side communication cost by 99%.

With the proliferation of cloud computing, outsourcing databases has become a common strategy for reducing on premise storage and computation costs. However, this approach raises serious privacy concerns, as sensitive data and query information may be exposed to the cloud. While existing top-$k$query methods have made progress in performance and privacy protection, they offer limited support for the more advanced requirement of diversity constrained queries. In light of this, we present SecDiv, the first privacy-preserving query system that supports diversity constraints over ciphertext in the cloud. SecDiv is built on a two-server distributed trust model and lightweight additive secret sharing, and hides data contents under an honest-but-curious, non-colluding adversary model to ensure that cloud servers learn no sensitive information. SecDiv comprises three customized secure components: SecDMap maps the structured database of the data owner into two secret-shared tables; SecQMap translates each SQL statement and its diversity constraints into vectors whose lengths match the database attributes, thereby hiding targeted attributes and literal values; and SecCQ performs secure filtering, ordering, and top-$k$selection in the cloud, centered on a secure most significant bit comparison implemented by a parallel-prefix adder. A formal security analysis is conducted to provide theoretical guarantees for the security of SecDiv. SecDiv is evaluated on three real datasets, with diversity constraints configured using top-$k$and category count conditions to emulate practical ranking scenarios. Compared with a plaintext baseline, SecDiv achieves identical results with 100% accuracy. Query latency remains practical, with second-level response times in typical settings, and communication overhead increases as expected. Overall, experimental results demonstrate that SecDiv attains a balanced trade-off among privacy, accuracy, and efficiency in real-world cloud service environments.

Recent advancements in text-to-speech and speech conversion technologies have enabled the creation of highly convincing synthetic speech. While these innovations offer numerous practical benefits, they also cause significant security challenges when maliciously misused. Therefore, there is an urgent need to detect these synthetic speech signals. Phoneme features provide a powerful speech representation for deepfake detection. However, previous phoneme-based detection approaches typically focused on specific phonemes, overlooking temporal inconsistencies across the entire phoneme sequence. In this paper, we develop a new mechanism for detecting speech deepfakes by identifying the inconsistencies of phoneme-level speech features. We design an adaptive phoneme pooling technique that extracts sample-specific phoneme-level features from frame-level speech data. By applying this technique to features extracted by pre-trained audio models on previously unseen deepfake datasets, we demonstrate that deepfake samples often exhibit phoneme-level inconsistencies when compared to genuine speech. To further enhance detection accuracy, we propose a deepfake detector that uses a graph attention network to model the temporal dependencies of phoneme-level features. Additionally, we introduce a random phoneme substitution augmentation technique to increase feature diversity during training. Extensive experiments on four benchmark datasets demonstrate the superior performance of our method over existing state-of-the-art detection methods.

As deep learning techniques advance rapidly, deepfake speech synthesized through text-to-speech or voice conversion networks is becoming increasingly realistic, posing significant challenges for detection and raising potential threats to social security. This growing realism has prompted extensive research in speech deepfake detection. However, current detection methods primarily focus on extracting features from either the raw waveform or the spectrogram, often overlooking the valuable correspondences between these two modalities that could enhance the detection of previously unseen types of deepfakes. In this work, we propose a multi-view collaborative learning network for speech deepfake detection, which jointly learns robust speech representations from both raw waveforms and spectrograms. Specifically, we first design a Dual-Branch Contrastive Learning (DBCL) framework for learning different view features. DBCL consists of two branches that learn representations from the raw waveform or the spectrogram and utilizes contrastive learning to enhance inter- and inner-view correlations. Additionally, we introduce a Waveform-Spectrogram Fusion Module (WSFM) to exchange multi-view information for collaborative learning. In the feature learning process, WSFM converts features between views and merges them adaptively using waveform-spectrogram cross-attention. The final detection is conducted based on the concatenation of the waveform and spectrogram features. We conduct extensive experiments on four benchmark deepfake speech detection datasets, and the experimental results demonstrate that our method can achieve better detection performance than current state-of-the-art detection methods.

Federated learning (FL) enables multiple clients to jointly train a model by sharing only gradient updates for aggregation instead of raw data. Due to the transmission of very high-dimensional gradient updates from many clients, FL is known to suffer from a communication bottleneck. Meanwhile, the gradients shared by clients as well as the trained model may also be exploited for inferring private local datasets, making privacy still a critical concern in FL. We present Clover, a novel system framework for communication-efficient, secure, and differentially private FL. To tackle the communication bottleneck in FL, Clover follows a standard and commonly used approach---top-k gradient sparsification, where each client sparsifies its gradient update such that only k largest gradients (measured by magnitude) are preserved for aggregation. Clover provides a tailored mechanism built out of a trending distributed trust setting involving three servers, which allows to efficiently aggregate multiple sparse vectors (top-k sparsified gradient updates) into a dense vector while hiding the values and indices of non-zero elements in each sparse vector. This mechanism outperforms a baseline built on the general distributed ORAM technique by several orders of magnitude in server-side communication and runtime, with also smaller client communication cost. We further integrate this mechanism with a lightweight distributed noise generation mechanism to offer differential privacy (DP) guarantees on the trained model. To harden Clover with security against a malicious server, we devise a series of lightweight mechanisms for integrity checks on the server-side computation. Extensive experiments show that Clover can achieve utility comparable to vanilla FL with central DP and no use of top-k sparsification. Meanwhile, achieving malicious security introduces negligible overhead in client-server communication, and only modest overhead in server-side communication and runtime, compared to the semi-honest security counterpart.

Graph learning has garnered increasing attention in recent years, which aims to train machine learning models over graph data to support various graph analytic tasks. Coming with the popularity of graph learning are critical privacy concerns regarding the information-rich graphs in many application domains (e.g., finance, social networks, and healthcare). There is thus an urgent call for privacy-preserving graph learning. In this paper, we target an emerging decentralized graph scenario, where a graph is fully decentralized among a set of nodes in such a way that each node only has a limited local view about the global graph. We propose PDGL, a new system framework that can effectively support privacy-assured model training over a decentralized graph, with privacy protection for the links among the nodes as well as the nodes’ private feature data and labels. In contrast to PDGL, prior work does not provide protection for the nodes’ links, feature data, and labels simultaneously. Extensive experiments demonstrate that while providing strong privacy protection for decentralized graph data, PDGL can achieve model utility comparable to the baseline setting of centralized graph learning.

Memristors can serve as variable synaptic weights between neurons for adaptive neural network regulation. Inspired by this, a discrete memristive Hopfield neural network (DM-HNN) is constructed utilizing an adaptive memristor weight instead of a fixed resistor weight. It has a line fixed point set with stability strongly related to the memristor initial state. On this basis, chaotic/hyperchaotic attractors with bifurcation dynamics are explored. Further, the memristor initial-boosting mechanism is examined and the memristor initial-boosted homogeneous attractors are elucidated. The results present that DM-HNN can exhibit chaotic/hyperchaotic attractors with intricate structures and memristor initial-boosted homogeneous attractors. Notably, the coexisting homogeneous sequences with excellent performance indices can be toggled by the memristor initial state, well reflecting the adaptive regulation of the memristor. Additionally, kinetic experiments on Field Programmable Gate Array (FPGA) verify the hardware implementability of DM-HNN, based on which an innovative memristor-state-based image encryption scheme is proposed, enabling resource-constrained scenarios and demonstrating excellent encryption performance.

Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries manipulate the training data to implant hidden backdoors, causing misclassifications when inputs are modified with adversary-specified triggers. To ensure the reliability of DNNs, it is crucial to repair backdoored models, ensuring all samples, including poisoned ones, are correctly predicted. Existing backdoor repair methods rely on recovering a universal trigger, which degrades the model performance on clean data and fails against sophisticated attacks. In this paper, we propose FixGuard, a novel backdoor repair method based on a min-min optimization formulation. FixGuard consists of two main stages: the trigger recovery stage and the backdoor unlearning stage. In the trigger recovery stage, FixGuard recovers a semantic-aware trigger for each class. Compared to using a universal trigger, class-wise triggers can identify malicious patterns more precisely while avoiding excessively large triggers that degrade model performance on clean data. In the backdoor unlearning stage, FixGuard generates synthesized poisoned samples by patching clean images with the recovered triggers while preserving their correct labels. Subsequently, it trains backdoored models on these synthesized samples to sever the malicious connection between triggers and target labels. Experimental results demonstrate that FixGuard effectively mitigates backdoor threats while preserving model performance.

Image splicing forgery is a frequent manipulation technique in digital image forensics, presenting a significant challenge for image integrity verification. Existing detection methods often fail to fully exploit noise features, particularly the valuable noise information that signals tampered regions within an image. To address this issue, we introduce a novel model for image splicing forgery detection called dual-branch noise-guided network (NoGNet). We design a noise information enhancement (NIE) module to capture subtle noise differences, thereby enhancing the representation of noise features that are critical for identifying tampered areas. Furthermore, we propose a multi-view feature fusion (MFF) module that integrates features from multiple perspectives, allowing the model to capture richer, more comprehensive feature representations. Extensive evaluations on four datasets demonstrate that NoGNet achieves state-of-the-art performance in splicing forgery detection. Robustness experiments confirm that NoGNet demonstrates effectiveness in detecting image splicing forgery under various common attacks.

Due to the properties of autonomy and scalability, decentralized storage networks (DSNs) leveraging blockchain technology have attracted growing attention. Integrity auditing and verifiable searchable encryption are two essential functions for DSNs. The former ensures reliable and fair storage services, while the latter enables users to conduct keyword searches over encrypted data and guarantees the public verifiability of search results. However, all existing research in DSN has focused either on integrity auditing or on verifiable searchable encryption separately. In this paper, we propose a novel scheme for encrypted decentralized storage that simultaneously supports verifiable search and integrity auditing. It employs a unified proof and supports one-time proof verification to validate both the correctness of the returned file identifiers and the integrity of the files associated with these identifiers. As a result, compared to previous schemes supporting only integrity auditing, our scheme maintains a similar proof size and the support for search result verification does not significantly increase the on-chain storage overhead. Additionally, our scheme allows users to dynamically update their outsourced files while ensuring forward security during the file insertion process. We formally analyze the correctness and security of our scheme, and implement a system prototype to evaluate its performance. The experimental results demonstrate that it achieves verifiable searchable encryption and integrity auditing with practically affordable overhead.

Secure communication techniques can protect data confidentiality during transmission through public channels. Chaotic systems are commonly used in secure communication due to their random-like behavior, unpredictability, and ergodicity. However, existing chaos-based secure communication schemes have some drawbacks concerning the chaotic systems used and the communication structures, so they cannot achieve satisfactory performance to resist transmission channel noise. In light of this, in this paper, we propose a two-dimensional (2D) cyclic chaotic system (2D-CCS) and design a novel chaos-based secure communication scheme called noise-reduced orthogonal frequency division multiplexing based differential chaos shift keying (NR-OFDM-DCSK). The 2D-CCS is a general framework that can generate a large number of new 2D chaotic maps using existing one-dimensional (1D) chaotic maps as seed maps. Theoretical analysis and experiment results demonstrate its robust chaotic behaviors. The NR-OFDM-DCSK employs a new chaotic map generated by 2D-CCS as the chaos generator, and its structure exhibits a strong ability to resist channel noise, as demonstrated by formulaic analysis. Our extensive experiments show that our developed 2D chaotic maps are more suitable for secure communication applications than existing 2D chaotic maps, and our NR-OFDM-DCSK can achieve a lower bit-error-rate (BER) than state-of-the-art secure communication schemes.

As deepfake technology poses severe threats to information security, significant efforts have been devoted to deepfake detection. To enable model generalization for detecting new types of deepfakes, it is required that the existing models should learn knowledge about new types of deepfakes without losing prior knowledge, a challenge known as catastrophic forgetting (CF). Existing methods mainly utilize domain adaptation to learn about the new deepfakes for addressing this issue. However, these methods are constrained to utilizing a small portion of data samples from the new deepfakes, and they suffer from CF when the size of the data samples used for domain adaptation increases. This resulted in poor average performance in source and target domains. In this paper, we introduce a novel approach to boost the generalizability of deepfake detection. Our approach follows a two-stage training process: training in the source domain (prior deepfakes that have been used for training) and domain adaptation to the target domain (new types of deepfakes). In the first stage, we employ expansive learning to train our expanded model from a well-trained teacher model. In the second stage, we transfer the expanded model to the target domain while removing assistant components. For model architecture, we propose the frequency extraction module to extract frequency features as complementary to spatial features and introduce spatial-frequency contrastive loss to enhance feature learning ability. Moreover, we develop a confidence judgement module to eliminate conflicts between new and prior knowledge. Experimental results demonstrate that our method can achieve better average accuracy in source and target domains even when using large-scale data samples of the target domain, and it exhibits superior generalizability compared to state-of-the-art methods.

Multi-replica storage is an advanced extension of traditional cloud storage that allows data owners to customize the number of backups for file blocks based on their relative importance. In such settings, remote auditing mechanisms are essential for verifying data integrity and ensuring that the cloud service provider (CSP) maintains the pre-negotiated number of replicas. However, existing schemes often expose block positions and backup quantities to the CSP, making users' data vulnerable to template attacks. Meanwhile, secure deduplication significantly reduces storage overhead and user costs while preserving data confidentiality. In this paper, we propose a novel multi-replica cloud storage scheme that, for the first time, simultaneously supports cross-user deduplication and integrity auditing in the ciphertext domain. The proposed scheme can not only protect data privacy from template attacks but also enable the elimination of redundant ciphertext replicas and audit authentication tags across users at the block level. Formal analysis validates the correctness and security guarantees of our scheme. Experimental results demonstrate its effectiveness with modest overhead.

The increasing sharing of images on social networks is prompting the involvement of digital watermarking to protect copyright and combat illegal redistribution. Owner-side embedding and client-side embedding are two modes of digital watermarking, among which the latter enables better system scalability than the former due to its higher owner-side efficiency. However, the existing client-side watermarking schemes do not take into account the preview needs of users, in which users are prevented from acquiring any visual information about the original image before decryption because it is encrypted to be fully blurred. As a result, users cannot select the desired one by previewing when a batch of encrypted images is given. To solve this problem, we overcome the incompatibility between techniques and innovatively combine client-side watermarking with thumbnail-preserving encryption to render the degraded visual perception of the original image onto the encrypted one. Specifically, the image is first fully encrypted as usual client-side watermarking, and then pixel adjustments are performed to approximate the sum of the original pixels in each block for rendering the degraded visual perception. In this way, two schemes with different performance emphasis are proposed, which implement watermark embedding based on spread spectrum and quantization index modulation separately. In terms of performance evaluation, the security of both schemes is thoroughly demonstrated, and experiments are conducted to assess their feasibility, robustness, and efficiency.

The widespread application of face recognition poses unprecedented threats to individual privacy, as face images can be easily and stealthily analyzed. Efforts have been made to employ adversarial perturbations to disrupt the automatic inference of unauthorized face recognition systems. However, existing schemes fail to satisfy the personalized protection requirements of individuals, which may diminish the user experience. In this paper, we propose a novel scheme that provides tailor-made face privacy protection for individuals via class-wise targeted universal adversarial perturbations (CT-UAPs). In our scheme, each individual can utilize a user-specific CT-UAP to exclusively generate protected faces whose identification outputs are a virtual identity predefined by themselves. For the generation of CT-UAPs, we develop an optimization-based method that guides the feature vectors of the protected faces to approach the class-wise feature space of the predefined virtual identity while simultaneously approaching that of the original identity. Extensive experiment results demonstrate the effectiveness of our scheme against five face recognition models. In addition, the interpretability of CT-UAPs is highlighted by the experimental results obtained through two-dimensional principal component analysis.

Deep neural networks (DNNs) are susceptible to backdoor attacks, where adversaries poison datasets with adversary-specified triggers to implant hidden backdoors, enabling malicious manipulation of model predictions. Dataset purification serves as a proactive defense by removing malicious training samples to prevent backdoor injection at its source. We first reveal that the current advanced purification methods rely on a latent assumption that the backdoor connections between triggers and target labels in backdoor attacks are simpler to learn than the benign features. We demonstrate that this assumption, however, does not always hold, especially in all-to-all (A2A) and untargeted (UT) attacks. As a result, purification methods that analyze the separation between the poisoned and benign samples in the input-output space or the final hidden layer space are less effective. We observe that this separability is not confined to a single layer but varies across different hidden layers. Motivated by this understanding, we propose FLARE, a universal purification method to counter various backdoor attacks. FLARE aggregates abnormal activations from all hidden layers to construct representations for clustering. To enhance separation, FLARE develops an adaptive subspace selection algorithm to isolate the optimal space for dividing an entire dataset into two clusters. FLARE assesses the stability of each cluster and identifies the cluster with higher stability as poisoned. Extensive evaluations on benchmark datasets demonstrate the effectiveness of FLARE against 22 representative backdoor attacks, including all-to-one (A2O), all-to-all (A2A), and untargeted (UT) attacks, and its robustness to adaptive attacks.

The prevalence of face capturing along with the advancement of face recognition poses a potential threat to individual privacy. To protect privacy, plenty of methods have been proposed to change identity in the face, thus blocking malicious face recognition. However, these methods fail to satisfy authentication requirements for special application scenarios, e.g., face authentication in surveillance capture. In this paper, we propose a novel face privacy protection model, which supports robust image authentication via information-conditional identity transformation. Specifically, we first introduce a basic face manipulation model (FMM), which can preserve identity-irrelevant attributes when manipulating identity. Based on FMM, we further design a lightweight protector called AIDPro, outputting a transformed identity which is different from the original one and is embedded a message presenting authentication information. Benefiting from the semantic robustness, our model does not require noise layers to achieve accurate message extraction after various image distortions. In addition, the message can be the condition to guide the identity transformation for privacy protection, which avoids extra resource consumption from supporting image authentication. Extensive experimental results demonstrate our model has comparable privacy protection performance, superior attribute preservation performance, and robust authentication performance especially in JPEG compression and screen shooting. Our code is available athttps://github.com/daizigege/AIDPro.

AI-synthesized speech, also known as deepfake speech, has recently raised significant concerns due to the rapid advancement of speech synthesis and speech conversion techniques. Previous works often rely on distinguishing synthesizer artifacts to identify deepfake speech. However, excessive reliance on these specific synthesizer artifacts may result in unsatisfactory performance when addressing speech signals created by unseen synthesizers. In this paper, we propose a robust deepfake speech detection method that employs feature decomposition to learn synthesizer-independent content features as complementary for detection. Specifically, we propose a dual-stream feature decomposition learning strategy that decomposes the learned speech representation using a synthesizer stream and a content stream. The synthesizer stream specializes in learning synthesizer features through supervised training with synthesizer labels. Meanwhile, the content stream focuses on learning synthesizer-independent content features, enabled by a pseudo-labeling-based supervised learning method. This method randomly transforms speech to generate speed and compression labels for training. Additionally, we employ an adversarial learning technique to reduce the synthesizer-related components in the content stream. The final classification is determined by concatenating the synthesizer and content features. To enhance the model’s robustness to different synthesizer characteristics, we further propose a synthesizer feature augmentation strategy that randomly blends the characteristic styles within real and fake audio features and randomly shuffles the synthesizer features with the content features. This strategy effectively enhances the feature diversity and simulates more feature combinations. Experimental results on four deepfake speech benchmark datasets demonstrate that our model achieves state-of-the-art robust detection performance across various evaluation scenarios, including cross-method, cross-dataset, and cross-language evaluations.

Chaotic systems have attracted extensive research due to their pseudorandomness, ergodicity, and unique properties. Most studies focus on chaotic systems in the real number domain, but recent research has explored the design of complex chaotic systems. However, the chaotic behaviors of previous complex chaotic systems can only be observed through experiments and lack theoretical proof. In this article, we construct a 2-D coupled complex chaotic (2D-CCC) map using two nonlinear functions in the complex number domain. We theoretically prove the robust and complex chaotic behavior of the 2D-CCC map using the Lyapunov exponent. In addition, we conduct extensive experiments to demonstrate the map's intricate dynamics and high performance indicators. Comparison results highlight its superiority over previous chaotic systems. We also implement our 2D-CCC map on a hardware platform to validate its implementation feasibility on hardware devices. Finally, we investigate the 2D-CCC map's application in pseudorandom number generation and the testing results validate the high degree of randomness in the generated pseudorandom numbers.

In this paper, we explore a new road for format-compatible 3D object encryption by proposing a novel mechanism of leveraging 2D image encryption methods. It alleviates the difficulty of designing 3D object encryption schemes coming from the intrinsic intricacy of the data structure, and implements the flexible and diverse 3D object encryption designs. First, turning complexity into simplicity, the vertex values, real numbers with continuous values, are converted into integers ranging from 0 to 255. The simplification result for a 3D object is a 2D numerical matrix. Second, six prototypes for three encryption patterns (permutation, diffusion, and permutation-diffusion) are designed as exemplifications to encrypt the 2D matrix. Third, the integer-valued elements in the encrypted numeric matrix are converted into real numbers complying with the syntax of the 3D object. In addition, some experiments are conducted to verify the effectiveness of the proposed mechanism.

Image splicing is a common technique used in image forgery. With the rapid development of digital image processing technology, detecting image splicing forgery has become increasingly challenging. Existing splicing forgery localization methods lack exploration in effectively utilizing tampered region boundary information. To address this issue, we propose a novel model for detecting image splicing forgery called boundary-assisted network (BASNet). We introduce a boundary-motivated module (BMM) to explore valuable and additional boundary features related to tampered regions, enhancing representation learning for detecting tampered regions. Additionally, we present a boundary-enhanced module (BEM) to enhance boundary information using the cross-channel attention mechanism. To efficiently merge features from various levels and boundary features, we further present the feature fusion module (FFM). To optimize performance, the BASNet incorporates weighted binary cross-entropy loss, dice loss, and boundary loss, which can effectively leverage edge supervision while mitigating imbalance between positive and negative samples. Evaluation of five widely-used forgery detection datasets demonstrates the state-of-the-art performance of the BASNet. Robustness experiments verify that the BASNet is robust enough to detect image splicing forgery across various common attacks.

In the realm of image security, there has been a burgeoning interest in harnessing deep learning techniques for the detection of digital image copy-move forgeries, resulting in promising outcomes. The generation process of such forgeries results in a distinctive topological structure among patches, and collaborative modeling based on these underlying topologies proves instrumental in enhancing the discrimination of ambiguous pixels. Despite the attention received, existing deep learning models predominantly rely on convolutional neural networks, falling short in adequately capturing correlations among distant patches. This limitation impedes the seamless propagation of information and collaborative learning across related patches. To address this gap, our work introduces an innovative framework for image copy-move forensics rooted in graph representation learning. Initially, we introduce an adaptive graph learning approach to foster collaboration among related patches, dynamically learning the inherent topology of patches. The devised approach excels in promoting efficient information flow among related patches, encompassing both short-range and long-range correlations. Additionally, we formulate a cascaded graph learning framework, progressively refining patch representations and disseminating information to broader correlated patches based on their updated topologies. Finally, we propose a hierarchical cross-attention mechanism facilitating the exchange of information between the cascaded graph learning branch and a dedicated forgery detection branch. This equips our method with the capability to jointly grasp the homology of copy-move correspondences and identify inconsistencies between the target region and the background. Comprehensive experimental results validate the superiority of our proposed scheme, providing a robust solution to security challenges posed by digital image manipulations.

Image splicing is a widely occurrence image tampering technology. With the rapid development of digital image processing technology, detecting image splicing forgery has become significantly challenging. Although various methods have been devised to identify such tampered images, existing approaches have not achieved optimal performance due to limitations in effectively leveraging feature maps of different scales. To address this issue, we propose a novel method for image splicing forgery detection called multi-scale feature attention fusion network (MFAF-Net). We propose a multi-scale atrous feature attention (MAFA) module designed to capture rich contextual features for multi-scale high-level feature fusion. Additionally, we present the multi-branch attention mechanism (MBAM) module to fuse contextual information from various branches for low-level features. This integration enhances the capability of low-level features to produce more refined pixel-level attention. We employ the weighted binary cross-entropy loss and dice loss in the MFAF-Net to overcome the imbalance between positive and negative samples. Extensive experiments demonstrate that the proposed MFAF-Net outperforms state-of-the-art methods. Robustness experiments also show our model exhibits image splicing forgery detection robustness under common attacks.

Time-series data is prevalent in many applications like smart homes, smart grids, and healthcare. And it is now increasingly common to store and query time-series data in the cloud. Despite the benefits, data privacy concerns in such outsourced services are pressing, making it imperative to embed privacy assurance mechanisms from the outset. Most existing related works have been focused on querying for different types of aggregate statistics. In this article, we instead focus on the secure support for advanced interval skyline queries, which allow to identify time series that are not dominated by any other time series within a query time interval. This is valuable for time-series data analytics in applications like remote health monitoring (e.g., identifying patients with high heart rates in a certain week). We present OblivTime, a new system framework for oblivious and efficient interval skyline query processing over encrypted time-series data. OblivTime is built from a synergy of time-series data analytics, lightweight cryptography, and GPU parallel computing, achieving stronger security guarantees and lower online query latency over the state-of-the-art prior work. Extensive experiments demonstrate that OblivTime can achieve up to$666\times$speedup in online query latency over the state-of-the-art prior work.

The proliferation of cloud computing has spurred the popularity of storing and querying databases in the cloud. Among others, skyline queries play an important role in the database field due to its usefulness in multi-criteria decision support systems. To accommodate the tailored needs of users, user-defined skyline query has recently emerged, allowing users to define custom preferences in their skyline queries. However, user-defined skyline query services, if deployed in the cloud, may raise critical privacy concerns as the outsourced databases and skyline queries may contain proprietary/privacy-sensitive information. In light of the above, this paper presents ObliuSky, a new solution enabling oblivious user-defined skyline query processing in the cloud. ObliuSky departs from prior work by not only providing confidentiality protection for the content of the outsourced database, the user-defined skyline queries, and the query results, but also hiding the data patterns (e.g., user-defined dominance relations among database points and search access patterns) which may indirectly cause data leakages. We formally analyze the security guarantees and conduct extensive performance evaluations. The results show that while achieving much stronger security guarantees than the state-of-the-art prior work, ObliuSky is superior in database and query encryption efficiency, and scalable in oblivious query processing.

Spatial crowdsourcing (SC) has recently emerged as a new crowdsourcing service paradigm, where workers move physically to designated locations to perform tasks. Most SC systems perform task assignment based on the spatial proximity between task locations and worker locations. Under such a strategy, workers can only perform tasks near them, which may result in low social welfare (i.e., the total profit of the platform and workers). In contrast, the newly emerging strategy of competitive task assignment (CTA) stimulates workers to compete for their preferred tasks, allowing optimization of the overall profit of SC systems. Among others, one novel CTA setting is competitive detour tasking, which allows workers to compete for tasks that need them to make detours from their original travel paths. However, it requires collecting each worker’s bidding profile which may expose private information. In light of this, in this article, we design, implement, and evaluate PrivCO, a new system framework enabling privacy-preserving competitive detour tasking services in SC. PrivCO delicately bridges state-of-the-art competitive detour tasking algorithms with lightweight cryptography, providing strong protections for workers’ bidding profiles. Extensive experiments over real-world datasets demonstrate that while offering strong security guarantees, PrivCO achieves social welfare comparable to the plaintext domain.

Deep neural network (DNN) models have been proven vulnerable to backdoor attacks. One trend of backdoor attacks is developing more invisible and dynamic triggers to make attacks stealthier. However, these invisible and dynamic triggers can be inadvertently mitigated by some widely used passive denoising operations, such as image compression, making the efforts under this trend questionable. Another trend is to exploit the full potential of backdoor attacks by proposing new triggering paradigms, such as hibernated or opportunistic backdoors. In line with these trends, our work investigates the first conditional backdoor attack, where the backdoor is activated by a specific condition rather than pre-defined triggers. Specifically, we take the JPEG compression as our condition and jointly optimize the compression operator and the target model's loss function, which can force the target model to accurately learn the JPEG compression behavior as the triggering condition. In this case, besides the conditional triggering feature, our attack is also stealthy and robust to denoising operations. Extensive experiments on the MNIST, GTSRB and CelebA verify our attack's effectiveness, stealthiness and resistance to existing backdoor defenses and denoising operations. As a new triggering paradigm, the conditional backdoor attack brings a new angle for assessing the vulnerability of DNN models, and conditioned over JPEG compression magnifies its threat due to the universal usage of JPEG.

Federated learning (FL) has rapidly become a compelling paradigm that enables multiple clients to jointly train a model by sharing only gradient updates for aggregation, without revealing their local private data. In order to protect the gradient updates which could also be privacy-sensitive, there has been a line of work studying local differential privacy (LDP) mechanisms to provide a formal privacy guarantee. With LDP mechanisms, clients locally perturb their gradient updates before sharing them out for aggregation. However, such approaches are known for greatly degrading the model utility, due to heavy noise addition. To enable a better privacy-utility trade-off, a recently emerging trend is to apply the shuffle model of DP in FL, which relies on an intermediate shuffling operation on the perturbed gradient updates to achieve privacy amplification. Following this trend, in this paper, we present Camel, a new communication-efficient and maliciously secure FL framework in the shuffle model of DP. Camel first departs from existing works by ambitiously supporting integrity check for the shuffle computation, achieving security against malicious adversary. Specifically, Camel builds on the trending cryptographic primitive of secret-shared shuffle, with custom techniques we develop for optimizing system-wide communication efficiency, and for lightweight integrity checks to harden the security of server-side computation. In addition, we also derive a significantly tighter bound on the privacy loss through analyzing the Rényi differential privacy (RDP) of the overall FL process. Extensive experiments demonstrate that Camel achieves better privacy-utility trade-offs than the state-of-the-art work, with promising performance.

Deep neural networks have demonstrated susceptibility to adversarial attacks. Adversarial defense techniques often focus on one-shot setting to maintain robustness against attack. However, new attacks can emerge in sequences in real-world deployment scenarios. As a result, it is crucial for a defense model to constantly adapt to new attacks, but the adaptation process can lead to catastrophic forgetting of previously defended against attacks. In this paper, we discuss for the first time the concept of continual adversarial defense under a sequence of attacks, and propose a life-long defense baseline called Anisotropic & Isotropic Replay (AIR), which offers three advantages: (1) Isotropic replay ensures model consistency in the neighborhood distribution of new data, indirectly aligning the output preference between old and new tasks. (2) Anisotropic replay enables the model to learn a compromise data manifold with fresh mixed semantics for further replay constraints and potential future attacks. (3) A straightforward regularizer mitigates the ‘plasticity-stability’ trade-off by aligning model output between new and old tasks. Experiment results demonstrate that AIR can approximate or even exceed the empirical performance upper bounds achieved by Joint Training.

Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries can maliciously trigger model misclassifications by implanting a hidden backdoor during model training. This paper proposes a simple yet effective input-level backdoor detection (dubbed IBD-PSC) as a ‘firewall’ to filter out malicious testing images. Our method is motivated by an intriguing phenomenon, i.e., parameter-oriented scaling consistency (PSC), where the prediction confidences of poisoned samples are significantly more consistent than those of benign ones when amplifying model parameters. In particular, we provide theoretical analysis to safeguard the foundations of the PSC phenomenon. We also design an adaptive method to select BN layers to scale up for effective detection. Extensive experiments are conducted on benchmark datasets, verifying the effectiveness and efficiency of our IBD-PSC method and its resistance to adaptive attacks. Codes are available at https://github.com/THUYimingLi/BackdoorBox.

As billions of face images stored on cloud platforms contain sensitive information to human vision, the public confronts substantial threats to visual face privacy. In response, the community has proposed some perturbation-based schemes to mitigate visual privacy leakage. However, these schemes need to generate a new protective perturbation for each image, failing to satisfy the real-time requirement of cloud platforms. To address this issue, we present an efficient visual face privacy protection scheme by utilizing person-specific veils, which can be conveniently applied to all images of the same user without regeneration. The protected images exhibit significant visual differences from the originals but remain identifiable to face recognition models. Furthermore, the protected images can be recovered to originals under certain circumstances. In the process of generating the veils, we propose a feature alignment loss to promote consistency between the recognition outputs of protected and original images with approximate construction of feature subspace. Meanwhile, the block variance loss is designed to enhance the concealment of visual identity information. Extensive experimental results demonstrate that our scheme can significantly eliminate the visual appearance of original images and almost has no impact on face recognition models.

Computer vision models based on deep neural networks are proven to be vulnerable to adversarial attacks. Robustness distillation, as a countermeasure, takes both robustness challenges and efficiency challenges of edge models into consideration. However, most existing robustness distillations are data-driven, which can hardly be deployed in data-privacy scenarios. Also, the trade-off between robustness and accuracy tends to transfer from the teacher to the student, and there has been no discussion on mitigating this trade-off in the data-free scenario yet. In this paper, we propose a Data-free Experts-guided Robustness Distillation (DERD) to extend robustness distillation to the data-free paradigm, which offers three advantages: (1) Dual-level adversarial learning strategy achieves robustness distillation without real data. (2) Expert-guided distillation strategy brings a better trade-off to the student model. (3) A novel stochastic gradient aggregation module reconciles the task conflicts of the multi-teacher from a consistency perspective. Extensive experiments demonstrate that the proposed DERD can even achieve comparable results to data-driven methods.

To implement encrypted data deduplication in a cloud storage system, users must encrypt files using special encryption algorithms (e.g., convergent encryption (CE)), which cannot provide strong protection. The confidential level of an outsourced file is determined by the user himself/herself subjectively or by the owner number of the file objectively. These files owned by a few users are considered strictly confidential and require strong protection. In this paper, we design, analyze and implement LSDedup, which attains a high storage efficiency while providing strictly confidential files (SCFiles) with strong protection. LSDedup allows cloud users to securely interact with cloud servers to check the confidential level of an outsourced file. Users encrypt the SCFiles using standard symmetric encryption algorithms to achieve a high security level, whereas encrypting the less confidential files (LSFiles) using CE such that cloud servers can perform deduplication. LSDedup is designed to prevent cloud servers reporting fake confidential level and a fake file user claiming the ownership of the file. Formal analysis is provided to justify its security. Besides, we implement an LSDedup prototype using Alibaba Cloud as backend storage. Our evaluations demonstrate that LSDedup can work with existing cloud service providers’ APIs and achieves modest performance overhead.

Compared with the continuous Hopfield neural network (HNN), the discrete HNN remains relatively under-explored in both academic and industrial domains. This paper proposes a simple two-dimensional (2-D) HNN-like map for neurons with specific internal decay. It is a discrete map with an infinite number of grid unstable points, leading to the appearance of grid homogeneous coexisting attractors. Theoretical analysis deduces the switching mechanism of initial-offsets, while numerical simulations disclose the grid homogeneous coexisting bifurcation behaviors and hyperchaotic attractors. The results manifest that 2-D HNN-like map can exhibit grid homogeneous coexisting hyperchaos in two dimensions and the highly random hyperchaotic sequences can be losslessly switched by two initial values. Additionally, hardware implementation on an STM32 platform validates the coexisting hyperchaotic attractors. Furthermore, using the initials-switched grid homogeneous coexisting hyperchaotic sequences, we develop a reliable and secure geolocation-based chaotic hardware encryptor. To our best knowledge, this is the first application of grid homogeneous coexisting hyperchaos in industrial field.

Designing chaotic maps and fractal maps with rich dynamics in the complex field presents an interesting and challenging research topic. In this paper, we propose a novel approach called the one-dimensional multi-valued model (1D-MVM) for generating 1D complex chaotic and fractal maps by levering both single-valued and multi-valued functions. To demonstrate the effectiveness of the 1D-MVM, we present two 1D complex chaotic maps and one 1D fractal map as specific examples. Theoretical analysis confirms that the chaotic maps generated by the 1D-MVM exhibit chaotic behavior, while property analysis reveals that these chaotic maps possess hyperchaotic strange attractors, with their associated Lyapunov exponents being determined by specific system parameters. We also conduct extensive experiments to demonstrate the intricate dynamics and high performance indicators of the newly generated complex chaotic maps. A hardware platform is developed and the principal value attractors of these chaotic maps are experimentally captured. In addition, we explore the application of the hyperchaotic sequences generated by these complex chaotic maps to the pseudo-random number generators. Rigorous testing results validate the high degree of randomness exhibited by the generated pseudo-random numbers. Finally, we leverage the second branch of the 1D fractal map to generate a diverse range of fractal structures, further demonstrating the versatility and potential applications of the proposed approach.

The onerous media sharing task prompts resource-constrained media owners to seek help from a cloud platform, i.e., storing media contents in the cloud and letting the cloud do the sharing. There are three key security/privacy problems that need to be solved in the cloud media sharing scenario, including data privacy leakage and access control in the cloud, infringement on the owner’s copyright, and infringement on the user’s rights. In view of the fact that no single technique can solve the above three problems simultaneously, two cloud media sharing schemes are proposed in this article, named FairCMS-I and FairCMS-II. By cleverly utilizing the proxy re-encryption technique and the asymmetric fingerprinting (AFP) technique, FairCMS-I and FairCMS-II solve the above three problems with different privacy/efficiency tradeoffs. Among them, FairCMS-I focuses more on cloud-side efficiency while FairCMS-II focuses more on the security of the media content, which provides owners with flexibility of choice. In addition, FairCMS-I and FairCMS-II also have advantages over existing cloud media sharing efforts in terms of optional indistinguishability under chosen-plaintext attack (IND-CPA) security and high cloud-side efficiency, as well as exemption from needing a trusted third party. Furthermore, FairCMS-I and FairCMS-II allow owners to reap significant local resource savings and thus can be seen as the privacy-preserving outsourcing of AFP. Finally, the feasibility and efficiency of FairCMS-I and FairCMS-II are demonstrated by experiments.

Deep neural networks (DNNs) are vulnerable to backdoor attacks, where a backdoored model behaves normally with clean inputs but exhibits attacker-specified behaviors upon the inputs containing triggers. Most previous backdoor attacks mainly focus on either the all-to-one or all-to-all paradigm, allowing attackers to manipulate an input to attack a single target class. Besides, the two paradigms rely on a single trigger for backdoor activation, rendering attacks ineffective if the trigger is destroyed. In light of the above, we propose a new M-to-N attack paradigm that allows an attacker to manipulate any input to attack N target classes, and each backdoor of the N target classes can be activated by any one of its M triggers. Our attack selects M clean images from each target class as triggers and leverages our proposed poisoned image generation framework to inject the triggers into clean images invisibly. By using triggers with the same distribution as clean training images, the targeted DNN models can generalize to the triggers during training, thereby enhancing the effectiveness of our attack on multiple target classes. Extensive experimental results demonstrate that our new backdoor attack is highly effective in attacking multiple target classes and robust against pre-processing operations and existing defenses.

Cloud service is a natural choice to store and manage the exponentially produced images. Data privacy is one of the most concerned points in cloud-based image services. Reversible data hiding over encrypted images (RDH-EI) is an effective technique to securely store and manage confidential images in the cloud. However, existing RDH-EI schemes have obvious weaknesses such as reliable key management system dependence and single point of failure. To securely store and manage confidential images in the cloud, in this study, we propose a new reversible data hiding strategy via image secret sharing. We first design a secure (r,n)-threshold preprocessing-free matrix secret sharing (PFMSS) technique. It can directly sharem-bit data by matrix multiplication without preprocessing. Using the PFMSS, we further design a secure (r,n)-threshold reversible data hiding scheme over encrypted images. The content owner divides a confidential image intonshares without accessing to a secret encryption key, and then sends thenshares toncloud-based image servers from competing providers. For each share, some additional data, e.g., integrity and identification of the image, can be embedded into it and these data can also be losslessly extracted. An authorized receiver can recover the confidential image fromrshares. By designing, the content owner doesn’t need to access a secret key when encrypting the image and the scheme can withstandn-rpoints of failure. Simulation results show that our scheme can ensure image content confidentiality and has a much larger embedding capacity compared to state-of-the-art schemes.

In cloud storage systems, secure deduplication plays a critical role in saving storage costs for the cloud server and ensuring data confidentiality for cloud users. Traditional secure deduplication schemes require users to encrypt their outsourced files using specific encryption algorithms that cannot provide semantic security. However, users are unable to directly benefit from the storage savings, as the relation between the actual storage cost and the offered prices remains not transparent. As a result, users may be unwilling to cooperate with the cloud by encrypting their data using semantically secure algorithms. Moreover, data integrity is a significant concern for cloud storage users. To address these issues, this paper proposes a novel transparent and secure deduplication scheme that supports integrity auditing. Compared to previous works, our design can verify the number of file owners and the integrity through one-time proof verification. It also protects the private contents of files and the privacy of file ownership from malicious users. Moreover, our scheme includes a batch auditing method to simultaneously verify the numbers of file owners and the integrity of multiple files. Theoretical analysis confirms the correctness and security of our scheme. Comparison results demonstrate its competing performance over previous solutions

Words are treated as atomic units in natural language processing tasks and it is a fundamental step to represent them as vectors for supporting subsequent computations. GloVe is a widely used machine learning model to train word vectors. Generally, a large corpus and high computation resources are required to train high-quality word vectors using GloVe, making it difficult for users to train their own word vectors by themselves. A natural choice nowadays is to outsource the training process to the cloud. However, coming with such cloud-based training services are serious privacy concerns, which should be well addressed. In this paper, we design, implement, and evaluate PPGloVe, the first system framework that supports privacy-preserving word vectors training using GloVe over encrypted data of multiple participants. We first decompose the training task and show that previous privacy-preserving machine learning techniques are not practical for this task. We then construct a new secure training strategy to delicately bridge lightweight cryptographic techniques with GloVe in depth to support privacy-preserving GloVe training on the cloud. By design, the corpora of the participants and the trained word vectors are kept private along the whole training process. Extensive experiments over three datasets of different scales demonstrate that PPGloVe produces word vectors with promising quality comparable to plaintext training, with practically affordable overhead.

With the growing popularity of cloud computing, sharing media data through the cloud has become a common practice. Due to high information redundancy, media data take up a significant amount of storage space. Moreover, similar media data may have the same visual effect, resulting in unnecessary duplication. Thus, it can greatly improve the cloud storage efficiency by performing deduplication to the similar media data stored on the cloud. However, data privacy is a growing concern in cloud-based service. In this paper, we present SimLESS, a secure deduplication system for similar data in cloud media sharing. SimLESS allows the cloud to perform deduplication over the encrypted similar media data of different distributors while protecting the confidentiality and ownership of the data. When uploading a media file, SimLESS allows the distributor to set a distance threshold, and the cloud performs deduplication only when there is a file on the cloud whose distance from the file being uploaded is smaller than the threshold. Additionally, we provide fine-grained access control for distributors to ensure that only authorized media consumers can access the data. Furthermore, our system prevents any distributor from claiming ownership of a media file using only the tag of a similar file. We formally analyze the security of SimLESS and implement a system prototype to evaluate its performance. Our experimental results demonstrate that the computation and communication costs of SimLESS are practically affordable.

The proliferation of portable camera devices, represented by smartphones, is increasing the risk of sensitive internal data being leaked by screen shooting. To trace the leak source, a lot of research has been done on screen-shooting resilient watermarking technique, which is capable of extracting the previously embedded watermark from the screen-shot image. However, all existing screen-shooting resilient watermarking schemes follow the owner-side embedding mode. In this mode, the management center will suffer heavy computational and communication burden in the case of numerous screens, which hinders the system scalability. As another embedding mode of digital watermarking, client-side embedding can solve the above scalability problem by migrating the watermark embedding operation to the same time when the screen decrypts the image. By designing a pair of image encryption and personalized decryption algorithms based on matrix operation, this paper is the first to realize the client-side embedding of screen-shooting resilient watermarking. In this implementation, challenges are overcome and the following key achievements are attained. First, our scheme embeds watermark using the algorithm of Fanget al. without modification, and thus fully inherits its robustness against screen shooting. Second, the original image is securely encrypted and the watermarked image can be directly retrieved through decryption. Third, the secrecy of the screen watermark is ensured by concealing the embedding pattern. Finally, our scheme is validated by experiments, which shows that the efficiency advantage of client-side embedding is realized while maintaining robustness.

The design of hyperchaotic systems with complex dynamics and ultrawide parameter space has been a research hotspot. In this article, we propose a 2-D threshold discrete map model and reveal its bifurcation behaviors and coexisting behaviors using several numerical methods. The proposed model with provable boundedness has multiple fixed points with parameter-related stability, and can produce amplitude-controllable hyperchaotic attractors with complex fractal structures and exhibit hyperchaotic dynamics with ultrawide parameter space. The hyperchaotic attractors are verified by the developed STM32 hardware prototype. In addition, a novel chaos-based timed one-time password scheme is presented and its generator is implemented through both software and hardware platforms. Multiplatform experiments verify the consistency of the one-time password, and randomness test also verifies the reliability of chaos-based timed one-time password generator. In summary, the proposed model easily achieves hyperchaotic regimes with ultrawide parameter space and delivers reliable performance for implementing the chaos-based timed one-time password.

Designing low-dimensional discrete maps with initial-dependent coexisting property is an attractive but challenging task. The coexisting property of a discrete map can be featured by initial-offset-control dynamics. To this end, this article proposes a two-dimensional discrete neuron model with sine activation function. The mechanisms of initial-offset-control coexisting dynamics are theoretically investigated and the homogenous coexisting behaviors are numerically revealed. The results show that the homogenous coexisting attractors are controlled along one direction by one initial-offset and along two directions by two initial-offsets. The former makes it model own finite invariant points, while the latter makes it own infinite invariant points. The homogenous coexisting hyperchaotic attractors are experimentally acquired on field programmable gate array digital platform. Besides, eight pseudorandom number generators (PRNGs) are designed using the proposed model under different parameter and initial settings, and the test results by TestU01 test suite show the high randomness of these PRNGs without chaos degradation.

Reversible data hiding over encrypted images (RDH-EI) technology is a viable solution for privacy-preserving cloud storage, as it enables the reversible embedding of additional data into images while maintaining image confidentiality. Since the data hiders, e.g., cloud servers, are willing to embed as much data as possible for storage, management, or other processing purposes, a large embedding capacity is desirable in an RDH-EI scheme. In this article, we introduce a novel bi-directional block encoding (BDBE) method, which, for the first time, encodes the distances of values in a binary sequence from both ends. This approach allows for encoding images with smaller sizes compared to traditional and state-of-the-art encoding methods. Leveraging the BDBE technique, we propose a high-capacity RDH-EI scheme. In this scheme, the content owner initially predicts the image pixels and then employs BDBE to encode the prediction errors, creating space for data embedding. The resulting encoded data are subsequently encrypted using a secure stream cipher, such as the Advanced Encryption Standard, before being transmitted to a data hider. The data hider can embed confidential information within the encrypted image for the purposes of storage, management, or other processing. Upon receiving the data, an authorized receiver can accurately recover the original image and the embedded data without any loss. Experimental results demonstrate that our RDH-EI scheme achieves a significantly larger embedding capacity compared to several state-of-the-art schemes.

Secure deduplication over encrypted data can greatly improve cloud storage efficiency and protect data privacy. Recently, there have been some research efforts aiming at designing secure deduplication schemes with the assistance of key servers (KSs). However, prior works are unsatisfactory in that they suffer from some limitations such as security degradation (the leakage at partial KSs will lead to all the ciphertexts being subject to offline brute-force attacks) or lack of scalability for handling the change of KSs. In this paper, we propose a new secure deduplication scheme for large-scale cloud storage service, which, to our best knowledge, is the first server-aided scheme that supports both tolerance of partial KSs leakage and dynamic change of KSs. Our scheme divides all the KSs into multiple groups and each KS group keeps a randomly generated secret key using threshold cryptography. We design a file-related KS group selection mechanism for assisting encryption key generation, which guarantees that the identical files of different users can be encrypted using the same keys. Our scheme is designed to update the KS groups regularly for supporting the joining and leaving of the KSs as well as maintaining long-term security. We leverage the blockchain to help divide KSs into groups in a fair way and securely migrate group secret keys during KS group updating. Formal analysis is provided to verify the correctness of our scheme and justify its security, and both theoretical and experimental results demonstrate that it has modest performance overhead.

Many service computing applications require real-time dataset collection from multiple devices, necessitating efficient sampling techniques to reduce bandwidth and storage pressure. Compressive sensing (CS) has found wide-ranging applications in image acquisition and reconstruction. Recently, numerous deep-learning methods have been introduced for CS tasks. However, the accurate reconstruction of images from measurements remains a significant challenge, especially at low sampling rates. In this paper, we propose Uformer-ICS as a novel U-shaped transformer for image CS tasks by introducing inner characteristics of CS into transformer architecture. To utilize the uneven sparsity distribution of image blocks, we design an adaptive sampling architecture that allocates measurement resources based on the estimated block sparsity, allowing the compressed results to retain maximum information from the original image. Additionally, we introduce a multi-channel projection (MCP) module inspired by traditional CS optimization methods. By integrating the MCP module into the transformer blocks, we construct projection-based transformer blocks, and then form a symmetrical reconstruction model using these blocks and residual convolutional blocks. Therefore, our reconstruction model can simultaneously utilize the local features and long-range dependencies of image, and the prior projection knowledge of CS theory. Experimental results demonstrate its significantly better reconstruction performance than state-of-the-art deep learning-based CS methods.

The fast development of deepfake generation technology has caused serious security threats to human society. Many deep-fake detection methods have been proposed recently, but most of them can only show high detection performance for the deepfakes generated by the similar techniques with the training dataset. To improve the ability of detecting unseen types of deepfakes, some deepfake detection methods have constructed self-generated datasets to train their models. However, the artifacts on these self-generated datasets are usually caused by some specific face-blending algorithms and lack of generality. In this paper, we propose cluster decision network (CDNet) to improve the deepfake detection generalizability. We design a selective attention module that decides the attention areas by manually cropping the facial areas (e.g., eyes, nose, and lips), which greatly reduce the model size and ensure a small model size. Inspired by the contrastive learning, we also propose a cluster classifier to equally utilize the feature representation. Extensive experiments show that our method outperforms existing state-of-the-art methods in deepfake detection generalizability and has the minimum model size.

Molecular classification benefits a lot from the re-cent success of graph contrastive learning (GCL) which pulls positive samples close and pushes the negative samples apart. GCL methods generate negative and positive samples via graph augmentation. Due to the structural corruption caused by graph augmentation, not all generated negative samples retain discrim-inative semantics. However, existing GCL methods ignore the difference between negative samples and hold an assumption that the importance of all negative samples is the same, leading to degraded performance of molecular classification. To address this issue, in this paper, we propose a novel molecular graph contrastive learning model (MocGCL) by selecting more useful negative samples to improve the performance of molecular classification. Specifically, we first employ different encoders to generate positive samples to improve the diversity of positive samples. Then, we design negative generation to generate negative samples and define semantic integrity to measure the usefulness of generated negative samples. Moreover, we propose the novel negative selection to dynamically select the negative samples of more usefulness to improve the molecular representation. In addition, we improve the contrastive loss to adaptively adjust the distance between selected negative samples, which can pre-serve the distinctive properties of selected negative samples in sample space. Extensive experiments on six typical bioinformatics datasets demonstrate the effectiveness of our MocGCL compared to most state-of-the-art methods.

With the large-scale deployment of the Internet of Things (IoT) in daily life, more and more privacy data are collected by IoT devices. These data are not directly physically controlled by users, which may cause privacy concerns. In fact, privacy has become one of the significant problems faced by IoT. In this article, we mainly study the protection of image privacy under the green IoT. We have conducted an in-depth analysis of the green IoT scenario and put forward the scope and corresponding goals that the scheme should have. Motivated by this, a novel image privacy protection scheme is proposed, i.e., heterogeneous and customized cost-efficient reversible image degradation for green IoT. This scheme fully considers the characteristics of privacy and the various users’ diverse requirements to achieve a heterogeneous and customized privacy protection. Meanwhile, cost effectiveness cannot be confined to the efficiency of the direct image processing at the expense of greatly increasing costs in other aspects, such as transmission and reversion. It is mitigated by preserving some visual content in the privacy-protected image. It also improves the image compression efficiency and ensures that the user can select the desired image according to the visual content for reversion. Some experiments have been carried out to demonstrate that this work has achieved the proposed scope and corresponding goals.

Inpainting the given region of an image is a typical requirement in computer vision. Conventional inpainting, through exemplar-based or diffusion-based strategies, can create realistic inpainted images at a very low cost. Also, such easy-to-use manipulation poses new security threats. Therefore, the detection of inpainting has attracted considerable attention from researchers. However, the existing methods are typically not suitable for the general detection of various inpainting algorithms. Motivated by this, in this work, an efficient feature enhancement network is proposed to locate the inpainted regions in the digital image. First, we design an artifact enhancement block to effectively capture the traces left by diffusion or exemplar-based inpainting. Then, the VGGNet is used as a feature extractor to describe advanced and low-resolution features. Finally, to take full advantage of enhanced features, we concatenate the features obtained by the feature extractor and the up-sampling operations. Extensive experimental evaluations, covering benchmarking, ablation, robustness, generalization, and efficiency studies, confirm the usefulness of the proposed method. This is especially true on the conventional inpainting dataset, our method obtains an average F1 score 7.63% higher than the second-best method. Theoretical and numerical analyses support the effectiveness of our feature enhancement network in representing the artifacts in inpainted images, exhibiting better potential for real-world forensics than various state-of-the-art strategies.

Cloud computing offers advantages in handling the exponential growth of images but also entails privacy concerns on outsourced private images. Reversible data hiding (RDH) over encrypted images has emerged as an effective technique for securely storing and managing confidential images in the cloud. Most existing schemes only work on uncompressed images. However, almost all images are transmitted and stored in compressed formats such as JPEG. Recently, some RDH schemes over encrypted JPEG bitstreams have been developed, but these works have some disadvantages such as a small embedding capacity (particularly for low quality factors), damage to the JPEG format, and file size expansion. In this study, we propose a permutation-based embedding technique that allows the embedding of significantly more data than existing techniques. Using the proposed embedding technique, we further design a large-capacity RDH scheme over encrypted JPEG bitstreams, in which a grouping method is designed to boost the number of embeddable blocks. The designed RDH scheme allows a content owner to encrypt a JPEG bitstream before uploading it to a cloud server. The cloud server can embed additional data (e.g., copyright and identification information) into the encrypted JPEG bitstream for storage, management, or other processing purpose. A receiver can losslessly recover the original JPEG bitstream using a decryption key. Comprehensive evaluation results demonstrate that our proposed design can achieve approximately twice the average embedding capacity compared to the best prior scheme while preserving the file format without file size expansion.

Traditional schemes for reversible data hiding in encrypted images (RDH-EI) focus on one data hider and cannot resist the single point of failure. Besides, the image security is determined by one party, rather than multiple parties. Thus, it is valuable to design RDH-EI schemes with multiple data hiders for stronger security. In this article, we propose a multiple data hiders-based RDH-EI scheme using a new secret sharing technique. First, we devise an$(r,n)$-threshold$(r\leq n)$matrix-based secret sharing (MSS) using matrix theory, and theoretically verify its efficacy and security properties. Then, using the MSS, we propose an$(r,n)$-threshold RDH-EI scheme called MSS-RDHEI. The content owner encrypts an image to be$n$encrypted images using the MSS with an encryption key, and outsources these encrypted images to$n$data hiders. Each data hider can embed some data, e.g., copyright and identification information, into the encrypted image for the purposes of storage, management, or other processing, and these data can also be losslessly extracted. An authorized receiver can recover the confidential image from$r$encrypted images. By designing, our MSS-RDHEI scheme can withstand$n-r$points of failure. Experimental results show that it ensures the image content confidentiality and achieves a much larger embedding capacity than state-of-the-art schemes.

Cloud computing promises great advantages in handling the exponential data growth. Secure deduplication can greatly improve cloud storage efficiency while protecting data confidentiality. In the meantime, when data are outsourced to the remote cloud, there is an imperative need to audit the integrity. Most existing works only consider the support for either secure deduplication or integrity auditing. Recently, there have been some research efforts aiming to integrate secure deduplication with integrity auditing. However, prior works are unsatisfactory in that they suffer from the leakage of ownership privacy and forgeability of auditing results for low-entropy data. In this paper, we propose a new scheme that delicately bridges secure deduplication and integrity auditing in encrypted cloud storage. In contrast with prior works, our scheme protects the ownership privacy and prevents the cloud service provider from forging the auditing results for low-entropy data. Furthermore, we propose a blockchain-based mechanism that helps to ensure key recoverability and reduce local storage cost of keys. Formal analysis is provided to justify the security guarantees. Experiment results demonstrate the modest performance overhead of our scheme.

Memristor is a special nonlinear circuit component with internal state and can lead to excellent chaos complexity in its constructed discrete system. To enhance the chaos complexity of a memristor-based discrete system, this article proposes a 2-D sine-transform-based (STB) memristive model. The model has line fixed point and its stability is dependent on memristor initial state. Complex dynamics with quasi-periodic bifurcation and multistability are demonstrated using numerical methods. For different control parameters, chaotic and hyperchaotic attractors are emerged and their complicated fractal structures and outstanding performance indicators are exhibited. A hardware prototype is developed and these attractors are experimentally captured therein. Besides, six pseudorandom number generators (PRNGs) are designed using the proposed model under different control parameters and the test results by the TestU01 standard show that these PRNGs have high randomness without chaos degradation. In brief, the proposed 2-D STB memristive model is flexible to generate chaos and hyperchaos with high performance.

As a significant extension of classical clustering methods, ensemble clustering first generates multiple basic clusterings and then fuses them into one consensus partition by solving a problem concerning graph partition with respect to the co-association matrix. Although the collaborative cluster structure among basic clusterings can be well discovered by ensemble clustering, most advanced ensemble clustering utilizes the self-representation strategy with the constraint of low-rank to explore a shared consensus representation matrix in multiple views. However, they still encounter two challenges: (1) high computational cost caused by both the matrix inversion operation and singular value decomposition of large-scale square matrices; (2) less considerable attention on high-order correlation attributed to the pursue of the two-dimensional pair-wise relationship matrix. In this article, based on low-rank and sparse decomposition from both matrix and tensor perspectives, we propose two novel multi-view ensemble clustering methods, which tangibly decrease computational complexity. Specifically, our first method utilizes low-rank and sparse matrix decomposition to learn one common co-association matrix, while our last method constructs all co-association matrices into one third-order tensor to investigate the high-order correlation among multiple views by low-rank and sparse tensor decomposition. We adopt the alternating direction method of multipliers to solve two convex models by dividing them into several subproblems with closed-form solution. Experimental results on ten real-world datasets prove the effectiveness and efficiency of the proposed two multi-view ensemble clustering methods by comparing them with other advanced ensemble clustering methods.

Recently, deep convolutional neural networks have been applied to image compressive sensing (CS) to improve reconstruction quality while reducing computation cost. Existing deep learning-based CS methods can be divided into two classes: sampling image at single scale and sampling image across multiple scales. However, these existing methods treat the image low-frequency and high-frequency components equally, which is an obstruction to get a high reconstruction quality. This paper proposes an adaptive multi-scale image CS network in wavelet domain called AMS-Net, which fully exploits the different importance of image low-frequency and high-frequency components. First, the discrete wavelet transform is used to decompose an image into four sub-bands, namely the low-low (LL), low-high (LH), high-low (HL), and high-high (HH) sub-bands. Considering that the LL sub-band is more important to the final reconstruction quality, the AMS-Net allocates it a larger sampling ratio, while allocating the other three sub-bands a smaller one. Since different blocks in each sub-band have different sparsity, the sampling ratio is further allocated block-by-block within the four sub-bands. Then a dual-channel scalable sampling model is developed to adaptively sample the LL and the other three sub-bands at arbitrary sampling ratios. Finally, by unfolding the iterative reconstruction process of the traditional multi-scale block CS algorithm, we construct a multi-stage reconstruction model to utilize multi-scale features for further improving the reconstruction quality. Experimental results demonstrate that the proposed model outperforms both the traditional and state-of-the-art deep learning-based methods.

Image recoloring is an emerging editing technique that can change the color style of an image by modifying pixel values without altering the original image content. With the rapid proliferation of social network and image editing techniques, recolored images (RIs) have raised new security issues in society. Existing detection methods have good performance in detecting RIs for certain categories of recoloring techniques. However, the performance on the handcrafted recoloring scenario is still poor due to the influence of human prior knowledge. To deal with this problem, we explore a solution from the perspective of chrominance texture artifacts to improve the generalization ability. The results of the analysis show that natural images (NIs) and RIs have textural disparities in different color components, especially in the chrominance components (i.e., Cb, Cr, and H). Based on such new prior knowledge of statistical discriminability, we propose a feature set to capture texture features in chrominance components for identifying RIs. Extensive experimental results show that the proposed method can accurately identify RIs with certain categories of recoloring techniques, and outperforms existing methods in the scenario of handcrafted recoloring.

Distributed fog computing has received increasing attention recently and fog-assisted cloud storage can provide a real-time service to collect and manage large-scale data for the applications of Internet of Things. Encrypted data deduplication over cloud storage can significantly save storage space of the cloud server while protecting the confidentiality of the outsourced data. Previous encrypted data deduplication schemes are mostly designed for traditional cloud storage with a two-layer architecture and cannot be applied to the emerging fog-assisted cloud storage that has a more complex three-layer architecture (i.e., cloud server, fog node and endpoint device). In this paper, we design, analyze and implement FCDedup, a new encrypted data deduplication scheme for fog-assisted cloud storage. FCDedup is a two-level deduplication system that enables each fog node to detect duplicated encrypted data uploaded by different endpoint devices, as well as enables cloud server to detect duplicated encrypted data from different fog nodes. By doing so, FCDedup can achieve both intra-deduplication within a single data owner and inter-deduplication across different data owners. FCDedup is also designed to prevent cloud server and fog nodes launching the brute-force attacks, and to guarantee the reliability of files downloaded from the cloud. Formal analysis is provided to justify its deduplication correctness and security. Besides, we implement a prototype of FCDedup using Alibaba Cloud as backend storage. Our evaluations demonstrate that FCDedup is completely compatible with existing cloud storage systems and achieves modest performance overhead.

Vertical federated learning (VFL) has recently emerged as an appealing distributed paradigm empowering multi-party collaboration for training high-quality models over vertically partitioned datasets. Gradient boosting has been popularly adopted in VFL, which builds an ensemble of weak learners (typically decision trees) to achieve promising prediction performance. Recently there have been growing interests in using decision table as an intriguing alternative weak learner in gradient boosting, due to its simpler structure, good interpretability, and promising performance. In the literature, there have been works on privacy-preserving VFL for gradient boosted decision trees, but no prior work has been devoted to the emerging case of decision tables. Training and inference on decision tables are different from that in the case of generic decision trees, not to mention gradient boosting with decision tables in VFL. In light of this, we design, implement, and evaluate Privet, the first system framework enabling privacy-preserving VFL service for gradient boosted decision tables. Privet delicately builds on lightweight cryptography and allows an arbitrary number of participants holding vertically partitioned datasets to securely train gradient boosted decision tables. Extensive experiments over several real-world datasets and synthetic datasets demonstrate that Privet achieves promising performance, with utility comparable to plaintext centralized learning.

High-dimensional (HD) chaotic map has wide applications in various research fields such as neural networks and secure communication. Designing HD chaotic maps with expected dynamics and robust hyperchaotic behaviors is an interesting but challenging topic. In this article, we propose an$n$-dimensional hyperchaotic map$(n\text{D}$-HCM) generation method on the basis of the Gershgorin-type theorem. First, the general form of the proposed$n\text{D}$-HCM is built using$n$parametric polynomials. Then, the entity and coefficient parameter matrices are configured according to the Gershorin-type theorem. Theoretical analysis shows that the generated$n\text{D}$-HCM has$n$positive Lyapunov exponents and thus can show robust hyperchaotic behaviors. Two examples of hyperchaotic map with specified equations are provided and their properties are analyzed to show the availability of the proposed method. Performance evaluations display that our$n\text{D}$-HCM possesses abundant properties and complex behaviors, and it can outperform some representative HD chaotic maps. Moreover, to show the application of our$n\text{D}$-HCM, we apply it to a secure communication scheme and the experimental results exhibit that it shows much better performance than these representative HD chaotic maps in resisting transmission noise.

Designing high-dimensional chaotic maps with expected dynamic properties is an attractive but challenging task. The dynamic properties of a chaotic system can be reflected by the Lyapunov exponents (LEs). Using the inherent relationship between the parameters of a chaotic map and its LEs, this paper proposes an$n$-dimensional polynomial chaotic system ($n\text{D}$-PCS) that can generate$n\text{D}$chaotic maps with any desired LEs. The$n\text{D}$-PCS is constructed from$n$parametric polynomials with arbitrary orders, and its parameter matrix is configured using the preliminaries in linear algebra. Theoretical analysis proves that the$n\text{D}$-PCS can produce high-dimensional chaotic maps with any desired LEs. To show the effects of the$n\text{D}$-PCS, two high-dimensional chaotic maps with hyperchaotic behaviors were generated. A microcontroller-based hardware platform was developed to implement the two chaotic maps, and the test results demonstrated the randomness properties of their chaotic signals. Performance evaluations indicate that the high-dimensional chaotic maps generated from$n\text{D}$-PCS have the desired LEs and more complicated dynamic behaviors compared with other high-dimensional chaotic maps. In addition, to demonstrate the applications of$n\text{D}$-PCS, we developed a chaos-based secure communication scheme. Simulation results show that$n\text{D}$-PCS has a stronger ability to resist channel noise than other high-dimensional chaotic maps.

Reversible data hiding in encrypted images (RDH-EIs) has attracted increasing attention since it can protect the privacy of original images while exactly extracting the embedded data. In this paper, we propose an RDH-EI scheme with multiple data hiders. First, we introduce a cipher-feedback secret sharing (CFSS) technique using the cipher-feedback strategy of the Advanced Encryption Standard. Then, using the CFSS technique, we devise a new$(r,n)$-threshold ($r\leq n$) RDH-EI scheme with multiple data hiders called CFSS-RDHEI. It can encrypt an original image into$n$encrypted images with reduced size using an encryption key and sends each encrypted image to one data hider. Each data hider can independently embed secret data into the encrypted image to obtain a marked encrypted image. The embedded data can be extracted from each marked encrypted image using the data hiding key, and the original image can be completely recovered from$r$marked encrypted images using the encryption key. Performance evaluations show that our CFSS-RDHEI scheme has a higher embedding rate and that its generated encrypted images are much smaller, while still being well protected, compared to existing secret sharing-based RDH-EI schemes.

With the nonlinearity and plasticity, memristors are widely used as nonlinear devices for chaotic oscillations or as biological synapses for neuromorphic computations. But discrete memristors (DMs) and their coupling maps have not received much attention, yet. Using a DM model, this article presents a general three-dimensional discrete memristor-based (3-D-DM) map model. By coupling the DM with four 2-D discrete maps, four examples of 3-D-DM maps with no or infinitely many fixed points are generated. We simulate the coupling coefficient-depended and memristor initial-boosted bifurcation behaviors of these 3-D-DM maps using numerical measures. The results demonstrate that the memristor can enhance the chaos complexity of existing discrete maps and its coupling maps can display hyperchaos. Furthermore, a hardware platform is developed to implement the 3-D-DM maps and the acquired hyperchaotic sequences have high randomness. Particularly, these hyperchaotic sequences can be applied to the auxiliary classifier generative adversarial nets for greatly improving the discriminator accuracy.

The magnetic induction effects have been emulated by various continuous memristive models but they have not been successfully described by a discrete memristive model yet. To address this issue, this article first constructs a discrete memristor and then presents a discrete memristive Rulkov (m-Rulkov) neuron model. The bifurcation routes of the m-Rulkov model are declared by detecting the eigenvalue loci. Using numerical measures, we investigate the complex dynamics shown in the m-Rulkov model, including regime transition behaviors, transient chaotic bursting regimes, and hyperchaotic firing behaviors, all of which are closely relied on the memristor parameter. Consequently, the involvement of memristor can be used to simulate the magnetic induction effects in such a discrete neuron model. Besides, we elaborate a hardware platform for implementing the m-Rulkov model and acquire diverse spiking-bursting sequences. These results show that the presented model is viable to better characterize the actual firing activities in biological neurons than the Rulkov model when biophysical memory effect is supplied.

When high-dimensional chaotic systems are applied to many practical applications, they are required to have robust and complex hyperchaotic behaviors. In this article, we propose a novel$n$D chaotic system construction method using the Pascal-matrix theory. First, a parametric Pascal matrix is constructed. Then, an$n$D chaotic system can be generated by using the parametric Pascal matrix as the parameter matrix of the system. Theoretical analysis shows that the generated$n$D chaotic systems have robust and complex chaotic behaviors, and they become$n$D Arnold Cat maps by fixing the parameters as some special values. Performance evaluations demonstrate that the$n$D chaotic systems have more complex chaotic behaviors and better distribution of outputs compared with existing HD chaotic systems. A 4-D Arnold Cat map and a 4-D chaotic map with hyperchaotic behaviors are generated as two examples. The two chaotic maps are then simulated on a microcontroller-based hardware platform and the chaotic sequences are tested to show good randomness.

Predicting the motion trajectories of moving agents in complex traffic scenes, such as crossroads and roundabouts, plays an important role in cooperative intelligent transportation systems. Nevertheless, accurately forecasting the motion behavior in a dynamic scenario is challenging due to the complex cooperative interactions between moving agents. Graph Convolutional Neural Network has recently been employed to deal with the cooperative interactions between agents. Despite the promising performance of resulting trajectory prediction algorithms, many existing graph-based approaches model interactions with an undirected graph, where the strength of influence between agents is assumed to be symmetric. However, such an assumption often does not hold in reality. For example, in pedestrian or vehicle interaction modeling, the moving behavior of a pedestrian or vehicle is highly affected by the ones ahead, while the ones ahead usually pay less attention to the ones behind. To fully exploit the asymmetric attributes of the cooperative interactions in intelligent transportation systems, in this work, we present a directed graph convolutional neural network for multiple agents trajectory prediction. First, we propose three directed graph topologies, i.e., view graph, direction graph, and rate graph, by encoding different prior knowledge of a cooperative scenario, which endows the capability of our framework to effectively characterize the asymmetric influence between agents. Then, a fusion mechanism is devised to jointly exploit the asymmetric mutual relationships embedded in constructed graphs. Furthermore, a loss function based on Cauchy distribution is designed to generate multimodal trajectories. Experimental results on complex traffic scenes demonstrate the superior performance of our proposed model when compared with existing approaches.

This paper addresses the multi-view subspace clustering problem and proposes the self-paced enhanced low-rank tensor kernelized multi-view subspace clustering (SETKMC) method, which is based on two motivations: (1) singular values of the representations and multiple instances should be treated differently. The reasons are that larger singular values of the representations usually quantify the major information and should be less penalized; samples with different degrees of noise may have various reliability for clustering. (2) many existing methods may cause the degraded performance when multi-view features reside in different nonlinear subspaces. This is because they usually assumed that multiple features lie within the union of several linear subspaces. SETKMC integrates the nonconvex tensor norm, self-paced learning, and kernel trick into a unified model for multi-view subspace clustering. The nonconvex tensor norm imposes different weights on different singular values. The self-paced learning gradually involves instances from more reliable to less reliable ones while the kernel trick aims to handle the multi-view data in nonlinear subspaces. One iterative algorithm is proposed based on the alternating direction method of multipliers. Extensive results on seven real-world datasets show the effectiveness of the proposed SETKMC compared to fifteen state-of-the-art multi-view clustering methods.

Multiview clustering as an important unsupervised method has been gathering a great deal of attention. However, most multiview clustering methods exploit theself-representation propertyto capture the relationship among data, resulting in high computation cost in calculating the self-representation coefficients. In addition, they usually employ different regularizers to learn the representation tensor or matrix from which a transition probability matrix is constructed in a separate step, such as the one proposed by Wuet al.. Thus, an optimal transition probability matrix cannot be guaranteed. To solve these issues, we propose a unified model for multiview spectral clustering by directly learning an adaptive transition probability matrix (MCA2M), rather than an individual representation matrix of each view. Different from the one proposed by Wuet al., MCA2M utilizes the one-step strategy to directly learn the transition probability matrix under the robust principal component analysis framework. Unlike existing methods using the absolute symmetrization operation to guarantee the nonnegativity and symmetry of the affinity matrix, the transition probability matrix learned from MCA2M is nonnegative and symmetric without any postprocessing. An alternating optimization algorithm is designed based on the efficient alternating direction method of multipliers. Extensive experiments on several real-world databases demonstrate that the proposed method outperforms the state-of-the-art methods.

When used in engineering applications, most existing chaotic systems may have many disadvantages, including discontinuous chaotic parameter ranges, lack of robust chaos, and easy occurrence of chaos degradation. In this article, we propose a two-dimensional (2-D) parametric polynomial chaotic system (2D-PPCS) as a general system that can yield many 2-D chaotic maps with different exponent coefficient settings. The 2D-PPCS initializes two parametric polynomials and then applies modular chaotification to the polynomials. Setting different control parameters allows the 2D-PPCS to customize its Lyapunov exponents in order to obtain robust chaos and behaviors with desired complexity. Our theoretical analysis demonstrates the robust chaotic behavior of the 2D-PPCS. Two illustrative examples are provided and tested based on numeral experiments to verify the effectiveness of the 2D-PPCS. A chaos-based pseudorandom number generator is also developed to illustrate the applications of the 2D-PPCS. The experimental results demonstrate that these examples of the 2D-PPCS can achieve robust and desired chaos, have better performance, and generate higher randomness pseudorandom numbers than some representative 2-D chaotic maps.

Image manipulation chain detection aims to identify the existence of involved operations and also their orders, playing an important role in multimedia forensics and image analysis. However,all the existing algorithms model the manipulation chain detection as a classification problem, and can only detect chains containing up to two operations. Due to the exponentially increased solution space and the complex interactions among operations, how to reveal a long chain from a processed image remains a long-standing problem in the multimedia forensic community. To address this challenge, in this paper, we propose a new direction for manipulation chain detection. Different from previous works, we treat the manipulation chain detection as a machine translation problem rather than a classification one, where we model the chains as the sentences of a target language, and each word serves as one possible image operation. Specifically, we first transform the manipulated image into a deep feature space, and further model the traces left by the manipulation chain as a sentence of a latent source language. Then, we propose to detect the manipulation chain through learning the mapping from the source language to the target one under a machine translation framework. Our method can detect manipulation chains consisting of up to five operations, and we obtain promising results on both the short-chain detection and the long-chain detection.

Chaotic systems are suitable for image encryption owing to their numerous intrinsic characteristics. However, chaotic maps and algorithmic structures employed in many existing chaos-based image encryption algorithms exhibit various shortcomings. To overcome these, in this study, we first construct a two-dimensional logistic tent modular map (2D-LTMM) and then develop a new colour image encryption algorithm (CIEA) using the 2D-LTMM, which is referred to as the LTMM-CIEA. Compared with the existing chaotic maps used for image encryption, the 2D-LTMM has a fairly wide and continuous chaotic range and more uniformly distributed trajectories. The LTMM-CIEA employs cross-plane permutation and non-sequential diffusion to obtain the diffusion and confusion properties. The cross-plane permutation concurrently shuffles the row and column positions of pixels within the three colour planes, and the non-sequential diffusion method processes the pixels in a secret and random order. The main contributions of this study are the construction of the 2D-LTMM to overcome the shortcomings of existing chaotic maps and the development of the LTMM-CIEA to concurrently encrypt the three colour planes of images. Simulation experiments and security evaluations show that the 2D-LTMM outperforms recently developed chaotic maps, and the LTMM-CIEA outperforms several state-of-the-art image encryption algorithms in terms of security.

Regarding as a basic circuit component with special nonlinearity, memristor has been widely applied in chaotic circuits and neuromorphic circuits. However, discrete memristor (DM) has not been received much attention, yet. To this end, this paper reports a general DM model and its unified mapping model. Using the general DM model, four representations of DMs are given and their pinched hysteresis loops are exhibited. Based on the unified DM mapping model, four two-dimensional (2D) DM maps are generated and their parameter-relied and initials-relied behaviors are explored using multiple numerical measures. The results demonstrate that all the four 2D DM maps can generate hyperchaos with coexisting bi-stable or memristor initial-boosted behavior, and their sequences have excellent performance indictors. A hardware device is constructed to implement these maps and the analog voltage signals are experimentally acquired. Moreover, pseudo-random number generators (PRNGs) are designed using these DM maps and the test results show that the generated pseudo-random numbers (PRNs) have high randomness.

When chaotic sequences are used in engineering applications, their oscillating amplitudes need to be adjusted nondestructively. To accommodate this issue, this article presents a simple 2-D sine map. It can not only generate the chaotic sequences with high complexity, but also boost the oscillating amplitudes by switching their initial states. To show the complex dynamics of the sine map, this article investigates its control parameters-related dynamical behaviors and initials-boosted coexisting bifurcations using numerical methods. The results demonstrate that the oscillating amplitudes of chaotic sequences generated by the sine map can be nondestructively controlled by switching their initial states. This makes the sine map more suitable for many chaos-based engineering applications. Furthermore, we develop a microcontroller-hardware test platform to implement the sine map. The experimental results show that the platform synchronously outputs multichannel initials-controlled chaotic sequences. We also design a pseudorandom number generator to explore the application of the sine map.

The low-rank tensor representation (LRTR) has become an emerging research direction to boost the multi-view clustering performance. This is because LRTR utilizes not only the pairwise relation between data points, but also the view relation of multiple views. However, there is one significant challenge: LRTR uses the tensor nuclear norm as the convex approximation but provides a biased estimation of the tensor rank function. To address this limitation, we propose the generalized nonconvex low-rank tensor approximation (GNLTA) for multi-view subspace clustering. Instead of the pairwise correlation, GNLTA adopts the low-rank tensor approximation to capture the high-order correlation among multiple views and proposes the generalized nonconvex low-rank tensor norm to well consider the physical meanings of different singular values. We develop a unified solver to solve the GNLTA model and prove that under mild conditions, any accumulation point is a stationary point of GNLTA. Extensive experiments on seven commonly used benchmark databases have demonstrated that the proposed GNLTA achieves better clustering performance over state-of-the-art methods.

In multi-view subspace clustering, the low-rankness of the stacked self-representation tensor is widely accepted to capture the high-order cross-view correlation. However, using the nuclear norm as a convex surrogate of the rank function, the self-representation tensor exhibits strong connectivity with dense coefficients. When noise exists in the data, the generated affinity matrix may be unreliable for subspace clustering as it retains the connections across inter-cluster samples due to the lack of sparsity. Since both the connectivity and sparsity of the self-representation coefficients are curial for subspace clustering, we propose a Reliable Multi-View Affinity Learning (RMVAL) method so as to optimize both properties in a single model. Specifically, RMVAL employs the low-rank tensor constraint to yield a well-connected yet dense solution, and purifies the densely connected self-representation tensor by preserving only the connections in local neighborhoods using the$l_1$-norm regularization. This way, the strong connections on the self-representation tensor are retained and the trivial coefficients corresponding to the inter-cluster connections are suppressed, leading to a “clean” self-representation tensor and also a reliable affinity matrix. We propose an efficient algorithm to solve RMVAL using the alternating direction method of multipliers. Extensive experiments on benchmark databases have demonstrated the superiority of RMVAL.

Robust chaos is defined as the inexistence of periodic windows and coexisting attractors in the neighborhood of parameter space. This characteristic is desired because a chaotic system with robust chaos can overcome the chaos disappearance caused by parameter disturbance in practical applications. However, many existing chaotic systems fail to consider the robust chaos. This article introduces an exponential chaotic model (ECM) to produce new one-dimensional (1-D) chaotic maps with robust chaos. ECM is a universal framework and can produce many new chaotic maps employing any two 1-D chaotic maps as base and exponent maps. As examples, we present nine chaotic maps produced by ECM, discuss their bifurcation diagrams and prove their robust chaos. Performance evaluations also show that these nine chaotic maps of ECM can obtain robust chaos in a large parameter space. To show the practical applications of ECM, we employ these nine chaotic maps of ECM in secure communication. Simulation results show their superior performance against various channel noise during data transmission.

Chaotic systems are widely employed in many practical applications for their significant properties. Existing chaotic systems may suffer from the drawbacks of discontinuous chaotic ranges and frail chaotic behaviors. To solve this issue, this paper proposes a two-dimensional (2D) sine chaotification system (2D-SCS). 2D-SCS can not only significantly enhance the complexity of 2D chaotic maps, but also greatly extend their chaotic ranges. As examples, this paper applies 2D-SCS to two existing 2D chaotic maps to obtain two enhanced chaotic maps. Performance evaluations show that these two enhanced chaotic maps have robust chaotic behaviors in much larger chaotic ranges than existing 2D chaotic maps. A microcontroller-based experiment platform is also designed to implement these enhanced chaotic maps in hardware devices. Furthermore, to investigate the application of 2D-SCS, these two enhanced chaotic maps are applied to design a pseudorandom number generator. Experiment results show that these enhanced chaotic maps can produce better random sequences than the existing 2D and several state-of-the-art one-dimensional (1D) chaotic maps.

Chaos is known as a natural candidate for cryptography applications owing to its properties such as unpredictability and initial state sensitivity. However, certain chaos-based cryptosystems have been proven to exhibit various security defects because their used chaotic maps do not have complex dynamical behaviors. To address this problem, this paper introduces a cosine-transform-based chaotic system (CTBCS). Using two chaotic maps as seed maps, the CTBCS can produce chaotic maps with complex dynamical behaviors. For illustration, we produce three chaotic maps using the CTBCS and analyze their chaos complexity. Using one of the generated chaotic maps, we further propose an image encryption scheme. The encryption scheme uses high-efficiency scrambling to separate adjacent pixels and employs random order substitution to spread a small change in the plain-image to all pixels of the cipher-image. The performance evaluation demonstrates that the chaotic maps generated by the CTBCS exhibit substantially more complicated chaotic behaviors than the existing ones. The simulation results indicate the reliability of the proposed image encryption scheme. Moreover, the security analysis demonstrates that the proposed image encryption scheme provides a higher level of security than several advanced image encryption schemes.

To securely communicate information in different networks, this paper introduces a novel secure communication scheme using non-coherent modulation. The transmitter is to generate transmitted signal by modulating the chaotic sequences and information bits, while the receiver can recover the information bits without generating a synchronized duplication of the chaotic sequence. Each frame can transmit two information bits. Thus, it can achieve a high transmission rate. Performance analysis demonstrates that the proposed communication scheme has high performance in resisting Gaussian noise and simulation results shows that it has better bit-error rate performance than a newly developed method in the additive-white-Gaussian-noise channel.

Generating chaotic maps with expected dynamics of users is a challenging topic. Utilizing the inherent relation between the Lyapunov exponents (LEs) of the Cat map and its associated Cat matrix, this paper proposes a simple but efficient method to construct an -dimensional ( -D) hyperchaotic Cat map (HCM) with any desired number of positive LEs. The method first generates two basic -D Cat matrices iteratively and then constructs the final -D Cat matrix by performing similarity transformation on one basic -D Cat matrix by the other. Given any number of positive LEs, it can generate an -D HCM with desired hyperchaotic complexity. Two illustrative examples of -D HCMs were constructed to show the effectiveness of the proposed method, and to verify the inherent relation between the LEs and Cat matrix. Theoretical analysis proves that the parameter space of the generated HCM is very large. Performance evaluations show that, compared with existing methods, the proposed method can construct -D HCMs with lower computation complexity and their outputs demonstrate strong randomness and complex ergodicity.

This paper proposes a general framework of 1-D chaotic maps called the dynamic parameter-control chaotic system (DPCCS). It has a simple but effective structure that uses the outputs of a chaotic map (control map) to dynamically control the parameter of another chaotic map (seed map). Using any existing 1-D chaotic map as the control/seed map (or both), DPCCS is able to produce a huge number of new chaotic maps. Evaluations and comparisons show that chaotic maps generated by DPCCS are very sensitive to their initial states, and have wider chaotic ranges, better unpredictability and more complex chaotic behaviors than their seed maps. Using a chaotic map of DPCCS as an example, we provide a field-programmable gate array design of this chaotic map to show the simplicity of DPCCS in hardware implementation, and introduce a new pseudo-random number generator (PRNG) to investigate the applications of DPCCS. Analysis and testing results demonstrate the excellent randomness of the proposed PRNG.

Different from existing methods that use matrix multiplications and have high computation complexity, this paper proposes an efficient generation method of${n}$-dimensional (${n}\text{D}$) Cat maps using Laplace expansions. New parameters are also introduced to control the spatial configurations of the${n}\text{D}$Cat matrix. Thus, the proposed method provides an efficient way to mix dynamics of all dimensions at one time. To investigate its implementations and applications, we further introduce a fast implementation algorithm of the proposed method with time complexity${O(n^{4})}$and a pseudorandom number generator using the Cat map generated by the proposed method. The experimental results show that, compared with existing generation methods, the proposed method has a larger parameter space and simpler algorithm complexity, generates${n}\text{D}$Cat matrices with a lower inner correlation, and thus yields more random and unpredictable outputs of${n}\text{D}$Cat maps.

In this paper, a new two-dimensional (2D) Tent cascade-Logistic map (2D-TCLM) is introduced. Analysis results demonstrate that it has complex chaotic behaviors. Using 2D-TCLM, a new image encryption algorithm is also proposed. Simulation results and security analysis show that it can encrypt different kinds of digital images into unrecognized random-like images with a high security level.

Chaotic maps are widely used in different applications. Motivated by the cascade structure in electronic circuits, this paper introduces a general chaotic framework called the cascade chaotic system (CCS). Using two 1-D chaotic maps as seed maps, CCS is able to generate a huge number of new chaotic maps. Examples and evaluations show the CCS's robustness. Compared with corresponding seed maps, newly generated chaotic maps are more unpredictable and have better chaotic performance, more parameters, and complex chaotic properties. To investigate applications of CCS, we introduce a pseudo-random number generator (PRNG) and a data encryption system using a chaotic map generated by CCS. Simulation and analysis demonstrate that the proposed PRNG has high quality of randomness and that the data encryption system is able to protect different types of data with a high-security level.

This paper introduces a new two-dimensional Logistic-Sine map (2D-LSM). It has excellent chaotic performance and its outputs are difficult to predict. Using 2D-LSM, this paper proposes a new image encryption algorithm. Simulation results and security analysis demonstrate that the proposed algorithm is able to protect different kinds of images with a high security level.