IEEE Transactions on Information Forensics and Security最新文献

{"title":"Grey-box Adversarial Attack on Communication in Communicative Multi-agent Reinforcement Learning","authors":"Xiao Ma, Wu-Jun Li","doi":"10.1109/tifs.2025.3560203","DOIUrl":"https://doi.org/10.1109/tifs.2025.3560203","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"35 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"De-anonymizing Monero: A Maximum Weighted Matching-Based Approach","authors":"Xingyu Yang, Lei Xu, Liehuang Zhu","doi":"10.1109/tifs.2025.3560193","DOIUrl":"https://doi.org/10.1109/tifs.2025.3560193","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"183 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revisiting Location Privacy in MEC-Enabled Computation Offloading","authors":"Jingyi Li, Wenzhong Ou, Bei Ouyang, Shengyuan Ye, Liekang Zeng, Lin Chen, Xu Chen","doi":"10.1109/tifs.2025.3558593","DOIUrl":"https://doi.org/10.1109/tifs.2025.3558593","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"60 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical Cross-Modal Image Generation for Multimodal Biometric Recognition With Missing Modality","authors":"Zaiyu Pan;Shuangtian Jiang;Xiao Yang;Hai Yuan;Jun Wang","doi":"10.1109/TIFS.2025.3559802","DOIUrl":"10.1109/TIFS.2025.3559802","url":null,"abstract":"Multimodal biometric recognition has shown great potential in identity authentication tasks and has attracted increasing interest recently. Currently, most existing multimodal biometric recognition algorithms require test samples with complete multimodal data. However, it often encounters the problem of missing modality data and thus suffers severe performance degradation in practical scenarios. To this end, we proposed a hierarchical cross-modal image generation for palmprint and palmvein based multimodal biometric recognition with missing modality. First, a hierarchical cross-modal image generation model is designed to achieve the pixel alignment of different modalities and reconstruct the image information of missing modality. Specifically, a cross-modal texture transfer network is utilized to implement the texture style transformation between different modalities, and then a cross-modal structure generation network is proposed to establish the correlation mapping of structural information between different modalities. Second, multimodal dynamic sparse feature fusion model is presented to obtain more discriminative and reliable representations, which can also enhance the robustness of our proposed model to dynamic changes in image quality of different modalities. The proposed model is evaluated on three multimodal biometric benchmark datasets, and experimental results demonstrate that our proposed model outperforms recent mainstream incomplete multimodal learning models.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4308-4321"},"PeriodicalIF":6.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Networked Control Systems Resilience Against DoS Attacks: A Data-Driven Approach With Adaptive Sampled-Data and Compression","authors":"Xiao Cai;Yanbin Sun;Xiangpeng Xie;Nan Wei;Kaibo Shi;Huaicheng Yan;Zhihong Tian","doi":"10.1109/TIFS.2025.3559464","DOIUrl":"10.1109/TIFS.2025.3559464","url":null,"abstract":"This paper addresses the critical challenge of achieving asymptotic stability in networked control systems (NCSs) under denial-of-service (DoS) attacks, focusing on maintaining security and stability within bandwidth-constrained environments. First, we construct a practical attack model using the NSL-KDD dataset to provide a realistic representation of DoS attack dynamics, capturing key attributes such as attack duration and frequency. Then, an iterative shrinkage-thresholding algorithm (ISTA) is introduced to supervise the adaptive sampled-data controller (ADSC), dynamically optimizing the sampling period to enhance control performance while minimizing communication overhead. To further mitigate the impact of DoS attacks, we propose a novel data compression mechanism that adapts to varying network conditions, ensuring efficient bandwidth utilization and preserving critical control data fidelity. In addition, the stability of the NCSs is rigorously verified through Lyapunov-Krasovskii functions (LKFs), demonstrating robust system behavior even under adverse network conditions. Finally, the effectiveness and practicality of the proposed approach are validated through experimental studies on a 2-degree-of-freedom (2-DoF) helicopter system, confirming its capability to ensure stability, optimize communication efficiency, and mitigate the effects of DoS attacks in real-world scenarios.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4100-4109"},"PeriodicalIF":6.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LightGBM-Based Audio Watermarking Robust to Recapturing and Hybrid Attacks","authors":"Zhaopin Su;Zhaofang Weng;Guofu Zhang;Chensi Lian;Niansong Wang","doi":"10.1109/TIFS.2025.3559408","DOIUrl":"10.1109/TIFS.2025.3559408","url":null,"abstract":"Digital audio watermarking is a critical technology widely used for copyright protection, content authentication, and broadcast monitoring. However, its robustness is significantly challenged by recapturing and hybrid attacks, which can easily remove watermarks. To address this issue, this work proposes a novel scheme based on the light gradient boosting machine (LightGBM), named LRAW (LightGBM-based Robust Audio Watermarking), which is designed to increase the robustness of audio watermarking against various attacks. Specifically, the scheme begins by analysing coefficients derived from the discrete wavelet transform (DWT), graph-based transform (GBT), and singular value decomposition (SVD). The extracted singular values consistently maintain a stable descending order even under recapturing attacks at a slightly greater distance. Leveraging this stability, the watermark information is implicitly embedded into the audio signal using a quantization rule. To simulate a hybrid attack scenario, a comprehensive feature dataset comprising 396,000 pieces of DWT-GBT-SVD feature data is constructed based on 60 original recordings and 9 types of attack. Furthermore, considering the distinct influences of embedding watermark bits 0 and 1 on the quantization of singular values, the watermark extraction process is formulated as a binary classification problem. LightGBM is trained using Bayesian optimization and the feature dataset to classify the watermark bits accurately. Finally, the complete watermark is recovered using a watermark sequence matching algorithm. Theoretical analysis and experimental results demonstrate that the proposed LRAW scheme outperforms state-of-the-art watermarking methods in robustness against various recapturing and hybrid attacks, even when the distance between the acoustic source and the receiver is considerable.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4212-4227"},"PeriodicalIF":6.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Charge Your Clients: Payable Secure Computation and Its Applications","authors":"Cong Zhang;Liqiang Peng;Weiran Liu;Shuaishuai Li;Meng Hao;Lei Zhang;Dongdai Lin","doi":"10.1109/TIFS.2025.3559456","DOIUrl":"10.1109/TIFS.2025.3559456","url":null,"abstract":"The online realm has witnessed a surge in the buying and selling of data, prompting the emergence of dedicated data marketplaces. These platforms cater to servers (sellers), enabling them to set prices for access to their data, and clients (buyers), who can subsequently purchase these data, thereby streamlining and facilitating such transactions. However, the current data market is primarily confronted with the following issues. Firstly, they fail to protect client privacy, presupposing that clients submit their queries in plaintext. Secondly, these models are susceptible to being impacted by malicious client behavior, for example, enabling clients to potentially engage in arbitrage activities. To address the aforementioned issues, we propose payable secure computation, a novel secure computation paradigm specifically designed for data pricing scenarios. It grants the server the ability to securely procure essential pricing information while protecting the privacy of client queries. Additionally, it fortifies the server’s privacy against potential malicious client activities. As specific applications, we have devised customized payable protocols for two distinct secure computation scenarios: Keyword Private Information Retrieval (KPIR) and Private Set Intersection (PSI). We implement our two payable protocols and compare them with the state-of-the-art related protocols that do not support pricing as a baseline. Since our payable protocols are more powerful in the data pricing setting, the experiment results show that they do not introduce much overhead over the baseline protocols. Our payable KPIR achieves the same online cost as baseline, while the setup is about <inline-formula> <tex-math>$1.3-1.6times $ </tex-math></inline-formula> slower than it. Our payable PSI needs about <inline-formula> <tex-math>$2times $ </tex-math></inline-formula> more communication cost than that of baseline protocol, while the runtime is <inline-formula> <tex-math>$1.5-3.2times $ </tex-math></inline-formula> slower than it depending on the network setting.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4183-4195"},"PeriodicalIF":6.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RaSA: Robust and Adaptive Secure Aggregation for Edge-Assisted Hierarchical Federated Learning","authors":"Lingling Wang;Mei Huang;Zhengyin Zhang;Meng Li;Jingjing Wang;Keke Gai","doi":"10.1109/TIFS.2025.3559411","DOIUrl":"10.1109/TIFS.2025.3559411","url":null,"abstract":"Secure Aggregation (SA), in the Federated Learning (FL) setting, enables distributed clients to collaboratively learn a shared global model while keeping their raw data and local gradients private. However, when SA is implemented in edge-intelligence-driven FL, the open and heterogeneous environments will hinder model aggregation, slow down model convergence speed, and decrease model generalization ability. To address these issues, we present a Robust and adaptive Secure Aggregation (RaSA) protocol to guarantee robustness and privacy in the presence of non-IID data, heterogeneous system, and malicious edge servers. Specifically, we first design an adaptive weights updating strategy to address the non-IID data issue by considering the impact of both gradient similarity and gradient diversity on the model aggregation. Meanwhile, we enhance privacy protection by preventing privacy leakage from both gradients and aggregation weights. Different from previous work, we address system heterogeneity in the case of malicious attacks, and the malicious behavior from edge servers can be detected by the proposed verifiable approach. Moreover, we eliminate the influence of straggling communication links and dropouts on the model convergence by combining efficient product-coded computing with repetition-based secret sharing. Finally, we perform a theoretical analysis that proves the security of RaSA. Extensive experimental results show that RaSA can ensure model convergence without affecting the generalization ability under non-IID scenarios. Moreover, the decoding efficiency of RaSA achieves <inline-formula> <tex-math>$1.33times $ </tex-math></inline-formula> and <inline-formula> <tex-math>$6.4times $ </tex-math></inline-formula> faster than the state-of-the-art product-coded and one-dimensional coded computing schemes.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4280-4295"},"PeriodicalIF":6.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143813885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extracting Private Training Data in Federated Learning from Clients","authors":"Jiaheng Wei, Yanjun Zhang, Leo Yu Zhang, Chao Chen, Shirui Pan, Kok-Leong Ong, Jun Zhang, Yang Xiang","doi":"10.1109/tifs.2025.3558581","DOIUrl":"https://doi.org/10.1109/tifs.2025.3558581","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"74 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Security Response Strategies Through Conjectural Online Learning","authors":"Kim Hammar;Tao Li;Rolf Stadler;Quanyan Zhu","doi":"10.1109/TIFS.2025.3558600","DOIUrl":"10.1109/TIFS.2025.3558600","url":null,"abstract":"We study the problem of learning adaptive security response strategies for an IT infrastructure. We formulate the interaction between an attacker and a defender as a partially observed, non-stationary game. We relax the standard assumption that the game model is correctly specified and consider that each player has a probabilistic conjecture about the model, which may be misspecified in the sense that the true model has probability 0. This formulation allows us to capture uncertainty and misconception about the infrastructure and the intents of the players. To learn effective game strategies online, we design Conjectural Online Learning (COL), a novel method where a player iteratively adapts its conjecture using Bayesian learning and updates its strategy through rollout. We prove that the conjectures converge to best fits, and we provide a bound on the performance improvement that rollout enables with a conjectured model. To characterize the steady state of the game, we propose a variant of the Berk-Nash equilibrium. We present COL through an intrusion response use case. Testbed evaluations show that COL produces effective security strategies that adapt to a changing environment. We also find that COL enables faster convergence than current reinforcement learning techniques.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4055-4070"},"PeriodicalIF":6.3,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10955193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}