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Correction to “A New Construction Method for Keystream Generators” IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-05-09 Çaǧdaş Gül, Orhun Kara
The authors would like to extend their apologies for the inadvertent inclusion of an erroneous index of the matrix ${M}$ for DIZY-80 in [1] . We sincerely regret any inconvenience caused by this typographical error and appreciate the chance to rectify it.
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Fair and Privacy-Preserved Data Trading Protocol by Exploiting Blockchain IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-05-08 Parhat Abla, Taotao Li, Debiao He, Huawei Huang, SongSen Yu, Yan Zhang
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Consistency of Stackelberg and Nash Equilibria in Three-Player Leader-Follower Games IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-05-06 Gehui Xu, Guanpu Chen, Zhaoyang Cheng, Yiguang Hong, Hongsheng Qi
There has been significant recent interest in a class of three-player leader-follower game models in many important cybersecurity scenarios. In such a tri-level hierarchical structure, a defender usually serves as a leader, dominating the decision process by the Stackelberg equilibrium (SE) strategy. However, such a leader-follower scheme may not always work, and the Nash equilibrium (NE) strategy
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Reconfigurable Intelligent Surface-Assisted Key Generation for Millimetre-Wave Multi-User Systems IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-05-06 Tianyu Lu, Liquan Chen, Junqing Zhang, Chen Chen, Trung Q. Duong
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Client-Side Embedding of Screen-Shooting Resilient Image Watermarking IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-05-06 Xiangli Xiao, Yushu Zhang, Zhongyun Hua, Zhihua Xia, Jian Weng
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
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Vulnerability Analysis of Distributed State Estimator Under False Data Injection Attacks IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-05-06 Pengyu Li, Dan Ye
This paper focuses on the vulnerability and strict vulnerability of distributed state estimators under false data injection (FDI) attacks, where adversaries aim to exert unbounded effects on the estimation error dynamics by injecting malicious data into sensor nodes, communication links, or both. In particular, a distributed system is characterized as vulnerable (or strictly vulnerable) if there exists
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Enhanced Few-Shot Malware Traffic Classification via Integrating Knowledge Transfer With Neural Architecture Search IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-05-03 Xixi Zhang, Qin Wang, Maoyang Qin, Yu Wang, Tomoaki Ohtsuki, Bamidele Adebisi, Hikmet Sari, Guan Gui
Malware traffic classification (MTC) is one of the important research topics in the field of cyber security. Existing MTC methods based on deep learning have been developed based on the assumption of enough high-quality samples and powerful computing resources. However, both are hard to obtain in real applications especially in availability of IoT. In this paper, we propose a few-shot MTC (FS-MTC)
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MEGR-APT: A Memory-Efficient APT Hunting System Based on Attack Representation Learning IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-05-02 Ahmed Aly, Shahrear Iqbal, Amr Youssef, Essam Mansour
The stealthy and persistent nature of Advanced Persistent Threats (APTs) makes them one of the most challenging cyber threats to uncover. Several systems adopted the development of provenance-graph-based security solutions to capture this persistent nature. Provenance graphs (PGs) represent system audit logs by connecting system entities using causal relations and information flows. Hunting APTs demands
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Improving Generalization of Deepfake Detectors by Imposing Gradient Regularization IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-05-02 Weinan Guan, Wei Wang, Jing Dong, Bo Peng
The rapid development of face forgery technology has posed a significant threat to information security. While deepfake detection has proven to be an effective countermeasure, it often struggles to detect fake images generated by unknown forgery methods. Thus, the generalization ability of deepfake detectors to unseen forgery data is a critical concern. Despite many efforts aimed at discovering new
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Performance Enhanced Secure Spatial Keyword Similarity Query With Arbitrary Spatial Ranges IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-05-02 Songnian Zhang, Rongxing Lu, Hui Zhu, Yandong Zheng, Yunguo Guan, Fengwei Wang, Jun Shao, Hui Li
The increasing prevalence of cloud computing drives the exploration of various secure query schemes over encrypted data, among which secure spatial keyword query has drawn a great deal of attention due to its broad application in location-based services. However, most existing schemes are either limited to the boolean keyword test or incapable of protecting access pattern privacy. Although the state-of-the-art
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CP-IPFE:Ciphertext-policy based inner product functional encryption IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-05-02 Haoxuan Yang, Changgen Peng
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CSI-RFF: Leveraging Micro-Signals on CSI for RF Fingerprinting of Commodity WiFi IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-05-02 Ruiqi Kong, He Chen
This paper introduces CSI-RFF, a new framework that leverages micro-signals embedded within C hannel S tate I nformation (CSI) curves to realize R adio- F requency F ingerprinting of commodity off-the-shelf (COTS) WiFi devices for open-set authentication. The micro-signals that serve as RF fingerprints are termed “micro-CSI”. Through experimentation, we have found that the presence of micro-CSI can
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Revocable and Privacy-Preserving Bilateral Access Control for Cloud Data Sharing IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-29 Mingyang Zhao, Chuan Zhang, Tong Wu, Jianbing Ni, Ximeng Liu, Liehuang Zhu
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Optimal Signaling for Covert Communications Under Peak Power Constraint IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-29 Bichen Kang, Neng Ye, Jianping An
Covert communications studied in prior works typically consider only the average power constraint on the transmit signal. In this paper, we explore the optimal signaling for covert communication under the peak power constraint, in view of the realistic limitation at the transmitter. Our main result is that the rate-optimal transmit signal distribution under the covertness constraint forms finite hyperspheres
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ForensicsForest Family: A Series of Multi-Scale Hierarchical Cascade Forests for Detecting GAN-Generated Faces IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-29 Jiucui Lu, Jiaran Zhou, Junyu Dong, Bin Li, Siwei Lyu, Yuezun Li
The prominent progress in generative models has significantly improved the authenticity of generated faces, raising serious concerns in society. To combat GAN-generated faces, many countermeasures based on Convolutional Neural Networks (CNNs) have been spawned due to their strong learning capabilities. In this paper, we rethink this problem and explore a new approach based on forest models instead
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MMQW: Multi-Modal Quantum Watermarking Scheme IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-29 Zheng Xing, Chan-Tong Lam, Xiaochen Yuan, Sio-Kei Im, Penousal Machado
To address the problem that existing quantum image watermarking schemes have only a single watermarking mode with weak robustness, in this paper we propose a novel Multi-Modal Quantum Watermarking (MMQW) scheme using the generalized model of novel enhanced quantum representation. Our scheme provides four quantum watermarking modes (G_G, G_C, C_C, C_G), covering both types of grayscale and color images
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Sparsity and Privacy in Secret Sharing: A Fundamental Trade-Off IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-26 Rawad Bitar, Maximilian Egger, Antonia Wachter-Zeh, Marvin Xhemrishi
This work investigates the design of sparse secret sharing schemes that encode a sparse private matrix into sparse shares. This investigation is motivated by distributed computing, where the multiplication of sparse and private matrices is moved from a computationally weak main node to untrusted worker machines. Classical secret-sharing schemes produce dense shares. However, sparsity can help speed
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Data Generation and Augmentation Method for Deep Learning-Based VDU Leakage Signal Restoration Algorithm IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-25 Taesik Nam, Dong-Hoon Choi, Euibum Lee, Han-Shin Jo, Jong-Gwan Yook
This study analyzes the phenomenon of electromagnetic (EM) leakage that occurs through cables and explores the potential for information forensics using deep learning-based image-processing algorithms. We focus on the transition-minimized differential signaling (TMDS) interface to analyze information leakage caused by the inherent differential signal synchronization errors in video graphics controllers
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Foolmix: Strengthen the Transferability of Adversarial Examples by Dual-Blending and Direction Update Strategy IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-25 Zhankai Li, Weiping Wang, Jie Li, Kai Chen, Shigeng Zhang
Adversarial example attacks are deemed to be a serious threat to deep neural network (DNN) models. Generating adversarial examples in white-box settings has been well-studied, however, it remains challenging to generate transferable adversarial examples that successfully attack black-box models. This work proposes Foolmix, a novel method for generating transferable adversarial examples for black-box
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Purified Authorization Service With Encrypted Message Moderation IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-24 Peng Jiang, Qi Liu, Liehuang Zhu
Access control encryption enables access control on both senders and receivers, and enhances message sanitization compared with traditionally cryptographic access control mechanisms. However, it is usually built on top of encrypted messages, which makes it difficult to identify malicious data and amplifies abusive message transmission. Message franking and source tracing mechanisms facilitate a report
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Causal Learning for Robust Specific Emitter Identification Over Unknown Channel Statistics IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-24 Peng Tang, Guoru Ding, Yitao Xu, Yutao Jiao, Yehui Song, Guofeng Wei
Specific emitter identification (SEI) is a device identification technology that extracts radio frequency (RF) fingerprint from received signals. However, channel effects on RF fingerprint can vary between the training and testing stage, and SEI based on deep learning (DL) will be unable to withstand channel changes. To address this problem, we propose a channel-robust SEI scheme driven by causal learning
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Contrast-Then-Approximate: Analyzing Keyword Leakage of Generative Language Models IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-22 Zhirui Zeng, Tao Xiang, Shangwei Guo, Jialing He, Qiao Zhang, Guowen Xu, Tianwei Zhang
There is an increasing tendency to fine-tune large-scale pre-trained language models (LMs) using small private datasets to improve their capability for downstream applications. In this paper, we systematically analyze the pre-train and then fine-tune the process of generative LMs and show that the fine-tuned LMs would leak sensitive keywords of the private datasets even without any prior knowledge
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Vulnerability Detection Based on Enhanced Graph Representation Learning IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-22 Peng Xiao, Qibin Xiao, Xusheng Zhang, Yumei Wu, Fengyu Yang
The detection of program vulnerabilities remains a challenging task in software security. The existing vulnerability detection methods rarely consider the multidimensional feature space complementarity of program graph structures, which easily overlooks contextual environment features and syntax structure features. This disadvantage leads to insufficient performance in capturing complex structural
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SeCoSe: Toward Searchable and Communicable Healthcare Service Seeking in Flexible and Secure EHR Sharing IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-22 Zhihuang Liu, Ling Hu, Zhiping Cai, Ximeng Liu, Yanhua Liu
Cloud-assisted electronic health record (EHR) sharing plays an important role in modern healthcare systems but faces threats of distrust and non-traceability. The advent of blockchain offers an attractive solution to overcome this issue. Many efforts are devoted to promoting secure, flexible, and multi-featured blockchain-based EHR sharing. Yet, the problem of seeking out suitable healthcare providers
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Privacy-Enhanced Frequent Sequence Mining and Retrieval for Personalized Behavior Prediction IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-22 Shuyu Chang, Zhenqi Shi, Fu Xiao, Haiping Huang, Xingchen Liu, Chaorun Sun
The widespread use of smartphones has yielded a wealth of behavioral sequence data from user interactions. These interactions offer insights into user preferences and patterns for personalized behavior prediction. However, there are some challenges in current privacy-preserving works for analyzing these data. These approaches have suboptimal service quality with smaller but longer datasets and insufficient
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PrivSSO: Practical Single-Sign-On Authentication Against Subscription/Access Pattern Leakage IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-22 Ge Gao, Yuan Zhang, Yaqing Song, Shiyu Li
Single-sign-on (SSO) authentication employs an identity provider (IdP) to provide users with an efficient way to authenticate themselves with different service providers and has been widely applied in digital systems. However, existing SSO authentication schemes suffer from critical issues in terms of security and privacy. Regarding security, most SSO authentication schemes achieve a high convenience
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The Dark Forest: Understanding Security Risks of Cross-Party Delegated Resources in Mobile App-in-App Ecosystems IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-19 Zhibo Zhang, Lei Zhang, Guangliang Yang, Yanjun Chen, Jiahao Xu, Min Yang
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Discrete Spectral Encryption of Single-Carrier Signals With Pseudo Random Dynamic Keys IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-18 Marcelo L. F. Abbade, Welerson S. Souza, Melissa O. Santos, Ivan E. L. Rodrigues, Ivan Aldaya, Luiz. H. Bonani, Murilo A. Romero
Physical layer security is a crucial step towards fully secure communications systems. The flexibility and ubiquity of digital signal processors in modern wireless and optical communication systems open up a clear path for the development of discrete-signals encryption techniques, which can be implemented relatively cheap. In this paper, we show the fundamental role of amplitude and phase encoding
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DP-Norm: Differential Privacy Primal-Dual Algorithm for Decentralized Federated Learning IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-18 Takumi Fukami, Tomoya Murata, Kenta Niwa, Iifan Tyou
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On the Impact of Voice Anonymization on Speech Diagnostic Applications: A Case Study on COVID-19 Detection IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-18 Yi Zhu, Mohamed Imoussaïne-Aïkous, Carolyn Côté-Lussier, Tiago H. Falk
With advances seen in deep learning, voice-based applications are burgeoning, ranging from personal assistants, affective computing, to remote disease diagnostics. As the voice contains both linguistic and para-linguistic information (e.g., vocal pitch, intonation, speech rate, loudness), there is growing interest in voice anonymization to preserve speaker privacy and identity. Voice privacy challenges
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Secret Multiple Leaders & Committee Election With Application to Sharding Blockchain IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-17 Mingzhe Zhai, Qianhong Wu, Yizhong Liu, Bo Qin, Xiaopeng Dai, Qiyuan Gao, Willy Susilo
Secret leader election in consensus could protect leaders from Denial of Service (DoS) or bribery attacks, enhancing the blockchain system security. Single Secret Leader Election (SSLE), proposed by Boneh et al., supports electing a single random leader from a group of nodes while the leader’s identity remains secret until he reveals himself. Subsequent research endeavors have introduced distinct approaches
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NeuralSanitizer: Detecting Backdoors in Neural Networks IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-17 Hong Zhu, Yue Zhao, Shengzhi Zhang, Kai Chen
Deep neural networks (DNNs) have been pervasively used in many areas, e.g., computer vision, speech recognition, natural language processing, etc. However, recent works show that they are vulnerable to backdoor/Trojan attacks, severely restricting their usage in various scenarios. In this paper, we propose NeuralSanitizer, a novel approach to detect and remove backdoors in DNNs, capable of capturing
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An Intelligent Reflecting Surface-Based Attack Scheme Against Dual-Functional Radar and Communication Systems IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-17 Beiyuan Liu, Jinjing Jiang, Qian Liu, Jiajia Liu, Sai Xu
Dual-functional radar and communication (DFRC) system is capable of sensing potential eavesdroppers close to the DFRC base station (BS) and further ensuring secure transmission using physical layer security technologies based on the obtained location information of eavesdroppers. However, such security can be threatened by a malicious intelligent reflecting surface (IRS) that simultaneously changes
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Boosting Black-Box Attack to Deep Neural Networks With Conditional Diffusion Models IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-17 Renyang Liu, Wei Zhou, Tianwei Zhang, Kangjie Chen, Jun Zhao, Kwok-Yan Lam
Existing black-box attacks have demonstrated promising potential in creating adversarial examples (AE) to deceive deep learning models. Most of these attacks need to handle a vast optimization space and require a large number of queries, hence exhibiting limited practical impacts in real-world scenarios. In this paper, we propose a novel black-box attack strategy, Conditional Diffusion Model Attack
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MalGNE: Enhancing the Performance and Efficiency of CFG-Based Malware Detector by Graph Node Embedding in Low Dimension Space IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-16 Hao Peng, Jieshuai Yang, Dandan Zhao, Xiaogang Xu, Yuwen Pu, Jianmin Han, Xing Yang, Ming Zhong, Shouling Ji
The rich semantic information in Control Flow Graphs (CFGs) of executable programs has made Graph Neural Networks (GNNs) a key focus for malware detection. However, existing CFG-based detection techniques face limitations in node feature extraction, such as information loss, neglect of execution sequence information, and redundancy in representation vectors. These limitations compromise the balance
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PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature Compensation IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-16 Lin Yuan, Wu Chen, Xiao Pu, Yan Zhang, Hongbo Li, Yushu Zhang, Xinbo Gao, Touradj Ebrahimi
The advancement of face recognition technology has delivered substantial societal advantages. However, it has also raised global privacy concerns due to the ubiquitous collection and potential misuse of individuals’ facial data. This presents a notable paradox: while there is a societal demand for a robust face recognition ecosystem to ensure public security and convenience, an increasing number of
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PUF-Assisted Radio Frequency Fingerprinting Exploiting Power Amplifier Active Load-Pulling IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-15 Yuepei Li, Kai Xu, Junqing Zhang, Chongyan Gu, Yuan Ding, George Goussetis, Symon K. Podilchak
This paper presents a novel radio frequency fingerprint (RFF) enhancement strategy by exploiting the physical unclonable function (PUF) to tune the RF hardware impairments in a unique and secure manner, which is exemplified by taking power amplifiers (PAs) in RF chains as an example. This is achieved by intentionally and slightly tuning the PA non-linearity characteristics using the active load-pulling
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RIS-Jamming: Breaking Key Consistency in Channel Reciprocity-Based Key Generation IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-15 Guyue Li, Paul Staat, Haoyu Li, Markus Heinrichs, Christian Zenger, Rainer Kronberger, Harald Elders-Boll, Christof Paar, Aiqun Hu
Channel Reciprocity-based Key Generation (CRKG) exploits reciprocal channel randomness to establish shared secret keys between wireless terminals. This new security technique is expected to complement existing cryptographic techniques for secret key distribution of future wireless networks. In this paper, we present a new attack, reconfigurable intelligent surface (RIS) jamming, and show that an attacker
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Understanding Visual Privacy Protection: A Generalized Framework With an Instance on Facial Privacy IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-15 Yushu Zhang, Junhao Ji, Wenying Wen, Youwen Zhu, Zhihua Xia, Jian Weng
With the widespread application of computer vision, the scenarios in terms of visual privacy have become increasingly diverse and meanwhile numerous studies have been conducted to address privacy concerns in these scenarios. However, these studies are individually tailored for specific scenarios, making their layouts challenging to be drawn upon easily. When encountering a new scenario, it takes significant
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Attribute-Guided Cross-Modal Interaction and Enhancement for Audio-Visual Matching IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-15 Jiaxiang Wang, Aihua Zheng, Yan Yan, Ran He, Jin Tang
Audio-visual matching is an essential task that measures the correlation between audio clips and visual images. However, current methods rely solely on the joint embedding of global features from audio clips and face image pairs to learn semantic correlations. This approach overlooks the importance of high-confidence correlations and discrepancies of local subtle features, which are crucial for cross-modal
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Dynamic Group Time-Based One-Time Passwords IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-08 Xuelian Cao, Zheng Yang, Jianting Ning, Chenglu Jin, Rongxing Lu, Zhiming Liu, Jianying Zhou
Group time-based one-time passwords (GTOTP) is a novel lightweight cryptographic primitive for achieving anonymous client authentication, which enables the efficient generation of time-based one-time passwords on behalf of a group without revealing any information about the actual client’s identity beyond their group membership. The security properties of GTOTP regarding anonymity and traceability
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Privacy-Preserving Multi-Biometric Indexing Based on Frequent Binary Patterns IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-08 Dailé Osorio-Roig, Lázaro Janier González-Soler, Christian Rathgeb, Christoph Busch
The development of large-scale identification systems that ensure the privacy protection of enrolled subjects represents a major challenge. Biometric deployments that provide interoperability and usability by including efficient multi-biometric solutions are a recent requirement. In the context of privacy protection, several template protection schemes have been proposed in the past. However, these
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BAGM: A Backdoor Attack for Manipulating Text-to-Image Generative Models IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-08 Jordan Vice, Naveed Akhtar, Richard Hartley, Ajmal Mian
The rise in popularity of text-to-image generative artificial intelligence (AI) has attracted widespread public interest. We demonstrate that this technology can be attacked to generate content that subtly manipulates its users. We propose a Backdoor Attack on text-to-image Generative Models (BAGM), which upon triggering, infuses the generated images with manipulative details that are naturally blended
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Practical Cyber Attack Detection With Continuous Temporal Graph in Dynamic Network System IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-04 Guanghan Duan, Hongwu Lv, Huiqiang Wang, Guangsheng Feng, Xiaoli Li
Deep learning (DL) greatly enhances cyber anomaly detection capabilities through effective statistical network characteristic. However, previous methods have not fully addressed two real-world scenario-driven challenges. 1) Frequent node access and disconnection sourced from free-bounded 5G/B5G cyberspace introduce unfamiliar communication behavior patterns, reducing the detection ability of the pre-trained
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FLPurifier: Backdoor Defense in Federated Learning via Decoupled Contrastive Training IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-04 Jiale Zhang, Chengcheng Zhu, Xiaobing Sun, Chunpeng Ge, Bing Chen, Willy Susilo, Shui Yu
Recent studies have demonstrated that backdoor attacks can cause a significant security threat to federated learning. Existing defense methods mainly focus on detecting or eliminating the backdoor patterns after the model is backdoored. However, these methods either cause model performance degradation or heavily rely on impractical assumptions, such as labeled clean data, which exhibit limited effectiveness
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AugSteal: Advancing Model Steal With Data Augmentation in Active Learning Frameworks IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-03 Lijun Gao, Wenjun Liu, Kai Liu, Jiehong Wu
With the proliferation of machine learning models in diverse applications, the issue of model security has increasingly become a focal point. Model steal attacks can cause significant financial losses to model owners and potentially threaten the security of their application scenarios. Traditional model steal attacks are primarily directed at soft-label black boxes, but their effectiveness significantly
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Secure Stabilization of Networked Lur’e Systems Suffering From DoS Attacks: A Resilient Memory-Based Event-Trigger Mechanism IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-02 Yanyan Ni, Zhen Wang, Yingjie Fan, Xia Huang, Hao Shen
This paper focuses on the exponential stabilization issue of networked Lur’e systems (NLSs) suffering from DoS attacks. To conserve limited network resources and withstand aperiodic DoS attacks, a resilient memory-based event-trigger (RMET) mechanism is firstly designed. Then, based on an in-depth discussion on the relationship between the RMET scheme and DoS attacks, a comprehensive closed-loop system
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Deeper Insight Into Why Authentication Schemes in IoT Environments Fail to Achieve the Desired Security IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-02 Yimin Guo, Yajun Guo, Ping Xiong, Fan Yang, Chengde Zhang
Designing an efficient and secure authentication scheme is a significant means to ensure the security of IoT systems. Hundreds of authentication schemes tailored for IoT environments have been proposed in recent years, and regrettably, many of them were soon found to have succumbed to security vulnerabilities. In an effort to investigate the underlying reason for this, Wang et al. (at TIFS’23) recently
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SStore: An Efficient and Secure Provable Data Auditing Platform for Cloud IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-01 Lipeng Wang, Mingsheng Hu, Zhijuan Jia, Zhi Guan, Zhong Chen
As more internet users opt to store their data in cloud storage, ensuring data integrity becomes a paramount concern. The emerging provable data possession (PDP) scheme enables auditors to verify data integrity with reduced bandwidth consumption compared to hash-based alternatives. Nevertheless, most existing PDP variants rely on a centralized node for generating or maintaining user keys, creating
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Dual Defense: Adversarial, Traceable, and Invisible Robust Watermarking Against Face Swapping IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-01 Yunming Zhang, Dengpan Ye, Caiyun Xie, Long Tang, Xin Liao, Ziyi Liu, Chuanxi Chen, Jiacheng Deng
Malicious applications of deep face swapping technology pose security threats such as misinformation dissemination and identity fraud. Some research propose the utilization of robust watermarking methods to track the copyright of facial images, facilitating post-forgery identity attribution. However, these methods cannot fundamentally prevent or eliminate the adverse impacts of face swapping. To address
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PHY-Layer Authentication Exploiting Channel Sparsity in MmWave MIMO UAV-Ground Systems IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-01 Yulin Teng, Pinchang Zhang, Xiao Chen, Xiaohong Jiang, Fu Xiao
This paper exploits the efficient channel modeling and channel sparsity to propose a novel Physical (PHY)-layer authentication framework for a Millimeter Wave (mmWave) Multiple-Input Multiple-Output (MIMO) Unmanned Aerial Vehicle (UAV)-ground system. Inspired by the Image Processing theory, we first explore a new Laplace prior approach for the efficient modeling of angular-domain mmWave MIMO channels
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Steganalysis of AI Models LSB Attacks IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-04-01 Daniel Gilkarov, Ran Dubin
Artificial intelligence has made significant progress in the last decade, leading to a rise in the popularity of model sharing. The model zoo ecosystem, a repository of pre-trained AI models, has advanced the AI open-source community and opened new avenues for cyber risks. Malicious attackers can exploit shared models to launch cyber-attacks. This work focuses on the steganalysis of injected malicious
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Defending Against Malicious Influence Control in Online Leader-Follower Social Networks IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-03-29 Liwang Zhu, Xiaotian Zhou, Jiahe Tian, Wei Li, Zhongzhi Zhang
The formation of opinions is fundamentally a network-based process, where the opinions of individuals in a social network exchange, evolve, and eventually converge towards a specific distribution. However, this dynamic process may be susceptible to manipulation by adversarial entities, who aim to maliciously influence the opinion formulation. The adversary may engage in extensive influence campaigns
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HomeSentinel: Intelligent Anti-Fingerprinting for IoT Traffic in Smart Homes IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-03-28 Beibei Li, Youtong Chen, Lei Zhang, Licheng Wang, Yanyu Cheng
Recent studies have demonstrated that malicious adversaries are capable of fingerprinting Internet of Things (IoT) devices in a smart home and further causing privacy breaches. However, many existing anti-fingerprinting schemes, either by traffic padding or traffic mutation, are less effective in defending against state-of-the-art fingerprinting methods. To meet this gap, we in this paper propose the
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Sweeper: Breaking the Validity-Latency Tradeoff in Asynchronous Common Subset IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-03-28 Guoyu Yang, Chang Chen, Qi Chen, Jianan Jiang, Jin Li, Debiao He
Asynchronous common subset (ACS) is an essential building block for Byzantine fault-tolerance and multi-party computation. The classic ACS framework is due to Ben-Or, Kemler, and Rabin (BKR), consisting of ${n}$ reliable broadcast (RBC) instances and ${n}$ asynchronous binary agreement (ABA) instances (where ${n}$ is the total number of replicas). Despite recent progresses of practical BKR-ACS, the
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Integral Pose Learning via Appearance Transfer for Gait Recognition IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-03-27 Panjian Huang, Saihui Hou, Chunshui Cao, Xu Liu, Xuecai Hu, Yongzhen Huang
Gait recognition plays an important role in video surveillance and security by identifying humans based on their unique walking patterns. The existing gait recognition methods have achieved competitive accuracy with shape and motion patterns under limited-covariate conditions. However, when extreme appearance changes distort discriminative features, gait recognition yields unsatisfactory results under
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SimLESS: A Secure Deduplication System Over Similar Data in Cloud Media Sharing IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-03-27 Mingyang Song, Zhongyun Hua, Yifeng Zheng, Tao Xiang, Xiaohua Jia
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
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MLaD²: A Semi-Supervised Money Laundering Detection Framework Based on Decoupling Training IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-03-25 Xuejiao Luo, Xiaohui Han, Wenbo Zuo, Xiaoming Wu, Wenyin Liu
Money laundering (ML) poses a severe threat to financial stability and social security. Various money laundering detection methods have emerged in the past two decades. Among these methods, some semi-supervised ones based on graph neural networks (GNNs) have achieved impressive performance. However, the homogeneity hypothesis of GNN-based methods does not fit the ML detection scenario, affecting the
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On Mixing Authenticated and Non-Authenticated Signals Against GNSS Spoofing IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-03-25 Francesco Ardizzon, Laura Crosara, Stefano Tomasin, Nicola Laurenti
Anti-spoofing techniques for current global navigation satellite systems (GNSS) authenticate signals on a single band and from a single system. However, nowadays commercial GNSS receivers commonly calculate the position, velocity, and time (PVT) solution by simultaneously utilizing signals from multiple constellations and bands, with a substantial enhancement in both accuracy and availability. Therefore
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A Unified Optimization Framework for Feature-Based Transferable Attacks IEEE Trans. Inform. Forensics Secur. (IF 6.8) Pub Date : 2024-03-25 Nanqing Xu, Weiwei Feng, Tianzhu Zhang, Yongdong Zhang
Despite the rapid progress and significant success of deep learning in a wide spectrum of fields, adversarial examples expose many security threats to deep learning models. Recently, an interesting property has been discovered that adversarial examples are transferable, which means adversarial examples targeting a given model can also attack another model. Therefore, many researchers are attracted