Object detection is a fundamental task in various applications ranging from autonomous driving to intelligent security systems. However, recognition of a person can be hindered when their clothing is decorated with carefully designed graffiti patterns, leading to the failure of object detection. To achieve greater attack potential against unknown black-box models, adversarial patches capable of affecting

Privacy-preserving range query, which allows the server to implement secure and efficient range query on encrypted data, has been widely studied in recent years. Existing privacy-preserving range query schemes can realize effective range query, but usually suffer from the low efficiency and security. In order to solve the above issues, we propose an Efficient and Privacy-preserving encode-based Range

Unmanned aerial vehicles (UAVs) have emerged as pivotal roles within internet of vehicles (IoV), serving as mobile base stations. However, while expanding coverage and improving mobility, the deployment of UAVs also poses a threat to the integrity and privacy of sensitive data due to open wireless communication channels in IoV. Therefore, preventing unauthorized access and data tampering is critically

The widespread collection and analysis of private speech signals have become increasingly prevalent, raising significant privacy concerns. To protect speech signals from unauthorized analysis, adversarial attack methods for deceiving speaker recognition models have been proposed. While a few of these methods are specifically designed for real-time protection of speech signals, they introduce significant

The emergence of advanced Deepfake technologies has gradually raised concerns in society, prompting significant attention to Deepfake detection. However, in real-world scenarios, Deepfakes often involve multiple faces. Despite this, most existing detection methods still detect these faces individually, overlooking the informative correlation between them and the relationship between the global information

Unsupervised person re-identification (ReID) aims at learning discriminative identity features for person retrieval without any annotations. Recent advances accomplish this task by leveraging clustering-based pseudo labels, but these pseudo labels are inevitably noisy, which deteriorates model performance. In this paper, we propose a Neighbour Consistency guided Pseudo Label Refinement (NCPLR) framework

Recent research has shown that adversarial samples are highly transferable and can be used to attack other unknown black-box Deep Neural Networks (DNNs). To improve the transferability of adversarial samples, several feature-based adversarial attack methods have been proposed to disrupt neuron activation in the middle layers. However, current state-of-the-art feature-based attack methods typically

Virtual reality (VR), offering 3D visuals and stereophonic sounds, significantly enhances users’ immersive experiences and has become a milestone in the era of the metaverse. However, due to the limited battery capacity of VR devices, it is common for users to rely on charging cables, which serve the dual purpose of power supply and audio output, to recharge their VR devices while in use. In this study

Dynamic searchable symmetric encryption (DSSE), as one of the promising cryptographic tools in cloud-based services, faces two crying needs at the age of multi-device. One is a lightweight client, and the other is robustness. A lightweight client facilitates seamless synchronization among multiple devices allowing users to feel as if they are operating on a single device, even on resource-constrained

Fingerprint presentation attack detection (FPAD) is essential in fingerprint identification systems. Noncontact methods such as fingerprint biometrics are becoming popular because they are not affected by skin conditions and there are no hygiene issues. However, most of the existing noncontact FPAD methods are supervised methods with poor generalizability and poor performance during events such as

This paper introduces a novel, lightweight deep invertible steganography network (LiDiNet) for image-in-image steganography. Traditional methods, while hiding a secret image within a cover image, often suffer from contour shadows or color distortion, making the secret image easily detectable. Additionally, the superposition of multiple invertible networks may complicate network structures and introduce

Ring signatures have been extensively researched for Cloud-assisted Electronic Medical Records (EMRs) sharing, aiming to address the challenge of “medical information silos” while safeguarding the privacy of patients’ personal information and the security of EMRs. However, most existing EMRs sharing systems that utilize ring signatures are vulnerable to quantum attacks, posing a severe challenge for

Federated Learning (FL), a secure and emerging distributed learning paradigm, has garnered significant interest in the Internet of Things (IoT) domain. However, it remains vulnerable to adversaries who may compromise privacy and integrity. Previous studies on privacy-preserving FL (PPFL) have demonstrated limitations in client model personalization and resistance to poisoning attacks, including Byzantine

Penetration testing, a crucial industrial practice for securing networked systems and infrastructures, has traditionally depended on the extensive expertise of human professionals. Addressing the scarcity of human experts, the development of automated penetration testing tools emerges as a promising avenue. Against the backdrop of rapid advancements in artificial intelligence technologies, reinforcement

Vulnerability detection in source code has been a focal point of research in recent years. Traditional rule-based methods fail to identify complex and unknown vulnerabilities, leading to poor performance. While deep learning (DL)-based methods have improved these shortcomings, there is still room for enhancement. For C/C++ source code, effective vulnerability detection requires considering both the

Federated learning (FL) is an emerging paradigm for privacy-preserving machine learning, in which multiple clients collaborate to generate a global model through training individual models with local data. However, FL is vulnerable to model poisoning attacks (MPAs) as malicious clients are able to destroy the global model by modifying local models. Although numerous model poisoning defense methods

Private set intersection (PSI) facilitates the computation of intersection between the private sets of two parties, ensuring that no additional information beyond the intersection itself is revealed. However, most state-of-the-art are limited to static PSI, leaving updatable PSI untouched. Existing PSI protocols will cost huge computational resources to compute intersection on updated sets. More seriously

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using

In recent years, palmprints have been extensively utilized for individual verification. The abundance of sensitive information in palmprint data necessitates robust protection to ensure security and privacy without compromising system performance. Existing systems frequently use cancelable transformations to protect palmprint templates. However, if an adversary gains access to the stored database,

PHY-layer spoofing attack is a potential critical issue in wireless network communication security, which could lead to catastrophic consequences for critical mission and applications, especially in Industrial Internet of Things scenarios with enormous number of devices. In this paper, we propose a novel spoofing attack detection scheme exploiting Channel State Information (CSI) phase difference. Firstly

The illegal use of facial forgery models, such as Generative Adversarial Networks (GAN) synthesized contents, has been on the rise, thereby posing great threats to personal reputation and national security. To mitigate these threats, recent studies have proposed the use of adversarial watermarks as countermeasures against GAN, effectively disrupting their outputs. However, the majority of these adversarial

Success Rate (SR) is one of the most popular side-channel security metrics measuring the efficiency of key recovery. Theoretical expression of success rate reveals the functional dependency between relevant parameters such as number of measurements and Signal-to-Noise Ratio (SNR), helping researchers understand the resistance of a given implementation rapidly. However so far, existing works have exposed

Multivariate time series anomaly detection (MTSAD) plays a crucial role in the Internet of Things (IoT) to identify device malfunction or system attacks. Graph neural networks (GNN) are widely applied in MTSAD to capture the spatial features among sensors. However, GNNs depend on a graph structure and explicit graph structures are not always available. To solve the problem of missing explicit graph

Distributed Outsourced Storage systems, exemplified by the InterPlanetary File System (IPFS), offer compelling alternatives to traditional centralized cloud storage by emphasizing resilience and openness. Advancing this paradigm, Decentralized Storage (DS) markets leverage distributed ledgers to facilitate the monetization of outsourced storage. However, these markets often prioritize security over

To reduce data storage costs, more individuals are using cloud servers for reliable, scalable, cost-effective, and globally accessible solutions. However, storing data in plaintext on cloud servers can lead to data leakage risks. Moreover, the advancement of quantum computing poses a threat to traditional encryption algorithms. To counter quantum computing attacks and enable searches over encrypted

Inaudible command injection transmits inaudible ultrasounds to inject adversarial speech commands into a voice assistant, therefore manipulating voice control systems (e.g., a garage door or a security camera) for illegitimate purposes. Although the attack is inaudible, we find it does leave visible “footprints”. Such attack “footprints” are the side product due to the interaction between the attack

The rapid growth of edge computing is accelerating data subscriptions between cloud platforms and mobile subscribers, but sensitive information in these data faces security and privacy concerns. Fortunately, matchmaking attribute-based encryption (MABE) as a new type of encrypted data filtering mechanism has been introduced in cloud edge, which not only enforces fine-grained access control over the