Generator-based adversarial attack methods aim to fool deep neural networks (DNNs) by training a generator for crafting adversarial examples (AEs). However, as DNNs evolve from Convolutional Neural Networks (CNNs) to Transformers, the existing generator-based methods can hardly achieve satisfactory attack performance against different target model architectures in semi-whitebox attack scenarios. In addition, the generated AEs are susceptible to various distortions (especially for JPEG compression with low quality factors), which deteriorate the attack ability and increase the unreliability. To address these issues, we propose a dual-branch guided generative model called Heterogeneous Encoding Network (HENet) to form a robust generator-based adversarial attack framework. Specifically, our HENet introduces an Adaptive Feature Fusion Module (AFFM) to solve the dimensions and representativeness contradictions between CNNs and Transformers, which steers the perturbation generation based on a richer latent space and achieves better general attack ability. To further improve the robustness against JPEG compression, we design and integrate a Dynamic Differentiable JPEG Simulator (DDJS), which introduces an adaptive quantization mask to determine the flow of the gradient backpropagation in each frequency position. Extensive experiments prove the proposed method achieves a better attack success rate, lower perturbation magnitude, and higher robustness for various target network architectures under compressed, distorted, and lossless scenarios. Our codes will be made publicly available.

Adversarial examples have been shown to deceive Deep Neural Networks (DNNs), raising widespread concerns about this security threat. More seriously, as different DNN models share critical features, feature-level attacks can generate transferable adversarial examples, thereby deceiving black-box models in real-world scenarios. Nevertheless, we have theoretically discovered the principle behind the limited transferability of existing feature-level attacks: Their attack effectiveness is essentially equivalent to perturbing features in one step along the direction of feature importance in the feature space, despite performing multiple perturbations in the pixel space. This finding indicates that existing feature-level attacks are inefficient in disrupting features through multiple pixel-space perturbations. To address this problem, we propose a P2FA that efficiently perturbs features multiple times. Specifically, we directly shift the perturbed space from pixel to feature space. Then, we perturb the features multiple times rather than just once in the feature space with the guidance of feature importance to enhance the efficiency of disrupting critical shared features. Finally, we invert the perturbed features to the pixels to generate more transferable adversarial examples. Numerous experimental results strongly demonstrate the superior transferability of P2FA over State-Of-The-Art (SOTA) attacks.

With the rapid development of the diffusion models, numerous exquisitely generated images have significantly increased the risk of image misuse and abuse. Despite various AI parties and companies having devoted themselves to embedding watermarks into the generated images to curb the potential detriments, the isolated embedding from the generation process makes the watermarks vulnerable to watermark removal networks. To address this issue, we propose a novel generative image watermark scheme, dubbed Diffusion Visible Watermark (DVW), which can generate watermarked images in one step without additional training or fine-tuning of the diffusion models. Specifically, DVW introduces a masked distribution alignment strategy to fuse the watermark distribution with a Gaussian noise distribution. By iterative denoising the fused aligned distribution with the pretraining diffusion models, the watermarked images with coordinated and unified distribution can be generated with natural robustness against removal. In addition, we design and integrate a dynamic transparency module to adaptively control the watermark coverage degree for better visual quality. Comprehensive experiments and analysis are conducted on two representative kinds of diffusion models, GLIDE and StableDiffusion, to prove the superior and generic robustness of our DVW against watermark removal without sacrificing the generation ability of the diffusion models.

Advances in image generation technologies have raised growing concerns about their potential misuse, particularly in producing misinformation and deepfakes. This creates an urgent demand for effective methods to detect AI-generated images (AIGIs). While progress has been made, achieving reliable performance across diverse generative models and scenarios remains challenging due to the absence of source-invariant features and the limited generalization of existing approaches. In this study, we investigate the potential of using image entropy as a discriminative cue for AIGI detection and propose Multi-granularity Local Entropy Patterns (MLEP), a set of feature maps computed based on Shannon entropy from shuffled small patches at multiple image scales. MLEP effectively captures pixel dependencies across scales and dimensions while disrupting semantic content, thereby reducing potential content bias. Based on MLEP, we can easily build a robust CNN-based classifier capable of detecting AIGIs with enhanced reliability. Extensive experiments in an open-world setting, involving images synthesized by 32 distinct generative models, demonstrate that our approach achieves substantial improvements over state-of-the-art methods in both accuracy and generalization. Our code and models are available at https://www.github.com/fkeufss/MLEP/.

Image‐to‐image ( I2I ) translation has emerged as a valuable tool for privacy protection in the digital age, offering effective ways to safeguard portrait rights in cyberspace. In addition, I2I translation is applied in real‐world tasks such as image synthesis, super‐resolution, virtual fitting, and virtual live streaming. Traditional I2I translation models demonstrate strong performance when handling similar datasets. However, when the domain distance between two datasets is large, translation quality may degrade significantly due to notable differences in image shape and edges. To address this issue, we propose Long‐Domain Search GAN ( LDSGAN ), an unsupervised I2I translation network that employs a GAN structure as its backbone, incorporating a novel Real‐Time Routing Search ( RTRS ) module and Sketch Loss. Specifically, RTRS aids in expanding the search space within the target domain, aligning feature projection with images closest to the optimization target. Additionally, Sketch Loss retains human visual similarity during long‐domain distance translation. Experimental results indicate that LDSGAN surpasses existing I2I translation models in both image quality and semantic similarity between input and generated images, as reflected by its mean FID and LPIPS scores of 31.509 and 0.581, respectively.

Invisible watermarking can be used as an important tool for copyright certification in the Metaverse. However, with the advent of deep learning, Deep Neural Networks (DNNs) have posed new threats to this technique. For example, artificially trained DNNs can perform unauthorized content analysis and achieve illegal access to protected images. Furthermore, some specially crafted DNNs may even erase invisible watermarks embedded within the protected images, which eventually leads to the collapse of this protection and certification mechanism. To address these issues, inspired by the adversarial attack, we introduce Invisible Adversarial Watermarking (IAW), a novel security mechanism to enhance the copyright protection efficacy of watermarks. Specifically, we design an Adversarial Watermarking Fusion Model (AWFM) to efficiently generate Invisible Adversarial Watermark Images (IAWIs). By modeling the embedding of watermarks and adversarial perturbations as a unified task, the generated IAWIs can effectively defend against unauthorized identification, access, and erase via DNNs and identify the ownership by extracting the embedded watermark. Experimental results show that the proposed IAW presents superior extraction accuracy, attack ability, and robustness on different DNNs, and the protected images maintain good visual quality, which ensures its effectiveness as an image protection mechanism.

Detection of aligned double Joint Photographic Experts Group (JPEG) compressed images is a crucial area of research within the field of digital image forensics. The detection tasks for aligned double JPEG compression can be categorized into two sub-tasks, namely detecting double JPEG images with the same quantization matrix (DJSQM) or double JPEG images with different quantization matrices (DJDQM). Existing methods for one of these sub-tasks may not be effective for the other. To address this issue, a novel approach is proposed by recompressing both DJDQM and DJSQM using modified quantization coefficients. The perturbation in the recompression process results in a perturbed error image, which is valid for both DJDQM and DJSQM. Subsequently, the relative change rate is used to combine the perturbed error image, the original error image, and the quantization error to derive the interference error and the interference quantization error. The interference error and interference quantization error further expand the difference between single and double compressed images by preserving the general validity of the original image information. Furthermore, the recompression process of DJDQM and DJSQM results in the conversion of truncation and rounding errors at the pixel level, which can be represented by the pixel state map. The pixel state map characterizes the differing transformation relationships between single and double compressed images and provides additional valid features, thereby enhancing the performance of the proposed method. The empirical results demonstrate that the proposed method outperforms existing methods on detecting aligned double JPEG compressed images.

Deep neural networks are vulnerable to adversarial examples. Although the adversarial example has superior white-box attack success rate, its transferability is poor under the black-box setting. Momentum is often integrated into attacks so as to prevent adversarial examples from overfitting the source model and improve the transferability of adversarial examples. How-ever, conventional momentum merely accumulates few gradients during the early iterations, resulting in the early adversarial examples already overfitting the source model. Therefore, we propose Experienced Momentum (EM), which is trained on a set of models derived by Random Channels Swapping (RCS). Since EM takes the direction of loss increasing for multiple models into account, assigning EM to the initial value of momentum to makes adversarial examples transferable across models during the early iterations. Moreover, conventional Nesterov momentum only take the previous gradients into consideration but ignore the gradient of the current data point during the whole pre-update, making the estimate of the next position imprecise. It prompts us to propose Precise Nesterov momentum (PN), which not only retains the looking-ahead property but also adopts the gradient of the current data point during the pre-update. To further improve transferability, we combine EM and PN as Experienced Precise Nesterov momentum (EPN). Extensive experiments on the ImageNet dataset against normally trained and defense models demonstrate that the proposed EPN is more effective than conventional momentum for improving transferability.

Smart logistics (s-Logistics) has become more and more popular driven by the intelligent Internet of Things (IoT) which deploys pervasive smart devices in s-Logistics systems. The explosive growth of s-Logistics data collected by these resource-limited IoT devices enables Fog-based s-Logistics that provides data outsourcing and sharing services via multiple clouds within small latency. Nevertheless, it also gives rise to prominent security risks of user privacy leakage considering malicious users and data integrity violation with untrusted cloud servers, which are severe to s-Logistics systems and cannot be addressed by simple encryption. To solve these issues, in this article, we propose an efficient large universe and traceable privacy-preserving data sharing (LUTPDS) for Fog-based s-Logistics. It simultaneously achieves data access control, data integrity protection, key escrow and abuse resistance, user privacy preserving, and scalability. We devise a large universe and multiauthority ciphertext-policy attribute-based encryption (CP-ABE) scheme in which access policy hiding mechanism is used for user privacy preserving, while white-box tracing and certificateless public data integrity auditing techniques are employed to resist key abuse and escrow problems. In addition, online/offline encryption and verifiable outsourced decryption are leveraged for high efficiency and cloud encryption is utilized to extend to multiple clouds. In the end, we formally prove the security of our scheme for indistinguishability of chosen plaintext attack (IND-CPA) security and traceability. Detailed performance evaluation with extensive experiments shows that our scheme is practicable for s-Logistics compared with the existing schemes.

Cloud-Fog-Assisted Internet-of-Things (IoT) is a convincing paradigm to provide users with on-demand and low-latency services through Fog nodes in the edge of multiple clouds (Multi-Cloud). Multi-Cloud is a scalable multi-domain service-oriented net-centric system and can respond to complicated user requirements leveraging Multi-Cloud Service Composition (MCSC). However, in MCSC, user security can be easily compromised by untrusted and curious cloud service providers that may collect and violate the privacy and other essential assets of cloud users. Although many trust-based MCSC solutions have been proposed to seek a trustworthy composite service with highest trust level, most of them are vulnerable to malicious users intending to break through the clouds and inflict serious data leakage or asset damage. Considering these security concerns on both malicious users and untrusted service providers, in this article, we present a trust-based secure multi-cloud collaboration framework for Cloud-Fog-Assisted IoT systems. Specifically, to guarantee the security of users, we develop a role-based trust evaluation method to enhance the trustworthiness of MCSC. To preserve the security of services, we design an efficient user authentication scheme and a secure collaboration scheme to provide collaborative user authentication and access control mechanism for MCSC. We develop a proof of concept implementation for our framework and demonstrate its practicability by performance evaluation with extensive experiments.

Nowadays, users upload numerous photos to social network platforms to share their daily lives. These photos contain numerous personal information, which can be easily captured by intelligent algorithms. To improve privacy security, we aim to form a protection mechanism by exploiting adversarial examples, which can mislead and disrupt intelligent algorithms. However, the existing adversarial attack lacks the study on recoverability and reversibility, which makes them unable to serve as an effective protection mechanism. To address this issue, we propose a recoverable generative adversarial network to generate self-recoverable adversarial examples. By modeling the adversarial attack and recovery as a united task, our method can minimize the error of the recovered examples while maximizing the attack ability, resulting in better recoverability of adversarial examples. To further boost the recoverability of these examples, we exploit a dimension reducer to optimize the distribution of adversarial perturbation. The experimental results prove that the adversarial examples generated by the proposed method present superior recoverability, attack ability, and robustness on different datasets and network architectures, which ensure its effectiveness as a protection mechanism in social networks.

With the development of Industrial Internet of Things (IIoT) and 5G, massive data are easily collected and transmitted in cloud. Therefore, it is critical to guarantee the security of data sharing. In IIoT applications, the users of a group are in hierarchical structure and they intend to access data by external groups, which requires fine-grained access control, data authenticity and data retrieval. However, existing approaches rarely provide such solutions to satisfy these requirements simultaneously. In this paper, we propose an efficient hierarchical and multi-group data sharing framework (HMGDSF) in cloud-assisted IIoT. Apart from fine-grained data access control for hierarchical users with key leakage resilience, HMGDSF achieves user anonymity with traceability and keyword-based data retrieval. Moreover, the approach supports data authenticity and integrity verification for multi-group data sharing by integrating group signature mechanism. We provide proof for the security of framework and demonstrate its efficiency and practicability by extensive evaluations.

Wireless networks ensure the unmanned aerial vehicles (UAVs) communicate and cooperate with each other, which plays an indispensable role among UAVs. The two crucial challenges in UAV wireless networks are energy saving and security. The current lightweight communication approaches lead to insufficient robustness of the encrypted transmission that is insecure. To address this issue, we propose a secure transmission approach with energy efficiency toward UAVs networks. We design a lightweight symmetric encryption algorithm based on SM4 and the relevant key negotiation and update mechanism to protect the confidentiality of communication contents. Moreover, a modified aggregative BLS signature scheme, together with the Merkle Hash tree (MHT), is introduced to guarantee the integrity and authenticity of data packets in transmission. Furthermore, we propose an online/offline revocable identity-based group signature (OORIBGS) scheme and integrate it into our framework for UAV anonymity, traceability, as well as revocability with small key management cost and high efficiency. We give detailed security analysis and prove that our proposal has the properties of data confidentiality, integrity, and authenticity, as well as identity traceability and anonymity. Moreover, we apply our approach in the UAVs networks and evaluate the runtime and anti-attack performance. The experimental results show that the proposed method can be effectively used in UAVs secure communication.

Fog-assisted Internet of Things (IoT) can outsource the massive data of resource-constraint IoT devices to cloud and fog nodes (FNs). Meanwhile, it enables convenient and low time-delay data-sharing services, which relies heavily on high security of data confidentiality and fine-grained access control. Many efforts have been focused on this urgent requirement by leveraging ciphertext-policy attribute-based encryption (CP-ABE). However, when deployed in fog-assisted IoT systems for secure data sharing, it remains a challenging problem of how to preserve attribute privacy of access policy, and trace-then-revoke traitors (i.e., malicious users intending to leak decryption keys for illegal profits) efficiently and securely in such a large scale and decentralized environment with resource-constraint user devices, especially in consideration of misbehaving cloud and FNs. Therefore, in this article, we propose a revocable and privacy-preserving decentralized data-sharing framework (RPDDSF) by designing a large universe and multiauthority CP-ABE scheme with fully hidden access policy for secure data sharing in IoT systems to achieve user attribute privacy preserving with unbounded attribute universe and key escrow resistance suitable for large scale and decentralized environment. Based on this, with RPDDSF, anyone can efficiently expose the traitors and punish them by forward/backward secure revocation. Besides, RPDDSF is able to guarantee data integrity for both data owners (DOs) and users to resist misbehaving cloud and FNs, alongwith low computation overhead for resource-constraint devices. Finally, RPDDSF is proven to be secure with detailed security proofs, and its high efficiency and feasibility are demonstrated by extensive performance evaluations.

With the assistance of the Internet of Things, the fast developing Healthcare Internet of Things (H-IoT) has promoted the healthcare ecosystem into the era of Health 5.0 and enables many promising medical applications, such as remote healthcare that is crucial in pandemic (e.g., coronavirus disease 2019). Healthcare participants can make accurate diagnosis, treatment, and research based on the shared personal health records (PHRs) sensed from remote H-IoT devices. However, current H-IoT systems fall short of a secure and trustworthy PHR sharing service in remote healthcare, which is able to prevent user privacy leakage and PHR integrity violation together with high efficiency in key distribution alongside efficient data retrieval and fine-grained access control. In response, we present a blockchain-based hierarchical data sharing framework (BHDSF) to provide fine-grained access control and efficient retrieval over encrypted PHRs with low consumed hierarchical key distribution and key leakage resistance. Compared with the existing solutions, the BHDSF takes both untrusted cloud and malicious auditor into consideration simultaneously and achieves trustworthy PHR integrity auditing and metadata verification by leveraging the blockchain technique. Besides, the BHDSF enables efficiently aggregative authentication for the trustworthiness of source records from H-IoT devices, which is lacked in most of the existing data sharing frameworks. Finally, we demonstrate the feasibility of the BHDSF by conducting extensive empirical tests over a real-world dataset.

Cloud-based Internet of Things, which is considered as a promising paradigm these days, can provide various applications for our society. However, as massive sensitive and private data in IoT devices are collected and outsourced to cloud for data storage, processing, or sharing for cost saving, the data security has become a bottleneck for its further development. Moreover, in many large-scale IoT systems, multiple group data sharing is practical for users. Thus, how to ensure data security in multiple group data sharing remains an open problem, especially the fine-grained access control and data integrity verification with public auditing. Therefore, in this paper, we propose a blockchain-based fine-grained data sharing scheme for multiple groups in cloud-based IoT systems. In particular, we design a novel multiauthority large universe CP-ABE scheme to guarantee the fine-grained access control and data integrity across multiple groups by integrating group signature into our scheme. Moreover, to ease the need for a trusted third auditor in traditional data public auditing schemes, we introduce blockchain technique to enable a distributed data public auditing. In addition, with the group signature, our scheme also realizes anonymity and traitor tracing. The security analysis and performance evaluation show that our scheme is practical for large-scale IoT systems.

Recently, cloud-based mobile crowdsensing (MCS) has developed into a promising paradigm which can provide convenient data sensing, collection, storage, and sharing services for resource-constrained terminates. Nevertheless, it also inflicts many security concerns such as illegal access toward user secret and privacy. To protect shared data against unauthorized accesses, many studies on Ciphertext-Policy Attribute-Based Encryption (CP-ABE) have been proposed to achieve data sharing granularity. However, providing a scalable and time-sensitive data-sharing scheme across hierarchical users with compound attribute sets and revocability remains a big issue. In this paper, we investigate this challenge and propose a hierarchical and time-sensitive CP-ABE scheme, named HTR-DAC, which is characteristics of time-sensitive data access control with scalability, revocability, and high efficiency. Particularly, we propose a time-sensitive CP-ABE for hierarchical structured users with recursive attribute sets. Moreover, we design a robust revocable mechanism to achieve direct user revocation in our scheme. We also integrate verifiable outsourced decryption to improve efficiency and guarantee correctness in decryption procedure. Extensive security and performance analysis is presented to demonstrate the security requirement satisfaction and high efficiency for our data-sharing scheme in MCS.

Mobile cloud computing (MCC) is embracing rapid development these days and able to provide data outsourcing and sharing services for cloud users with pervasively smart mobile devices. Although these services bring various conveniences, many security concerns such as illegally access and user privacy leakage are inflicted. Aiming to protect the security of cloud data sharing against unauthorized accesses, many studies have been conducted for fine‐grained access control using ciphertext‐policy attribute‐based encryption (CP‐ABE). However, a practical and secure data sharing scheme that simultaneously supports fine‐grained access control, large university, key escrow free, and privacy protection in MCC with expressive access policy, high efficiency, verifiability, and exculpability on resource‐limited mobile devices has not been fully explored yet. Therefore, we investigate the challenge and propose an Efficient and Multiauthority Large Universe Policy‐Hiding Data Sharing (EMA‐LUPHDS) scheme. In this scheme, we employ fully hidden policy to preserve the user privacy in access policy. To adapt to large scale and distributed MCC environment, we optimize multiauthority CP‐ABE to be compatible with large attribute universe. Meanwhile, for the efficiency purpose, online/offline and verifiable outsourced decryption techniques with exculpability are leveraged in our scheme. In the end, we demonstrate the flexibility and high efficiency of our proposal for data sharing in MCC by extensive performance evaluation.