IEEE Transactions on Information Forensics and Security最新文献

{"title":"Throughput Improvement for RIS-Empowered Wireless Powered Anti-Jamming Communication Networks (WPAJCN)","authors":"Zheng Chu;David Chieng;Chiew Foong Kwong;Huan Jin;Zhengyu Zhu;Chongwen Huang;Chau Yuen","doi":"10.1109/TIFS.2025.3563818","DOIUrl":"10.1109/TIFS.2025.3563818","url":null,"abstract":"In this paper, we propose a reconfigurable intelligent surface (RIS)-aided wireless powered anti-jamming communication network (WPAJCN), where the RIS is utilized to participate in downlink wireless power transfer (WPT), as well as uplink anti-jamming wireless information transfer (AJ-WIT). To evaluate the network anti-jamming performance, we maximize a sum anti-jamming throughput, with the constraints of downlink WPT and uplink AJ-WIT time scheduling, and unit-modulus RIS phase shifts. The formulated problem is not convex in terms of these two types of coupled variables, which cannot be directly solved. To address this problem, the Lagrange dual method and Karush-Kuhn-Tucker conditions are presented to transform its sum-of-logarithmic objective function into the logarithmically fractional counterpart, which reformulate the original problem into that with respect to RIS phase shift vectors and WPT time scheduling. Next, we propose to apply the Dinkelback algorithm to solve a non-linear fractional programming with respect to the downlink WPT and uplink AJ-WIT RIS phase shifts in an alternating fashion, each of which is derived into a semi-closed solution by utilizing the Riemannian Manifold Optimization (RMO). In addition, the optimal WPT time scheduling is obtained by numerical search. Finally, the numerical results are demonstrated to confirm the improved performance of the proposed approach compared to the benchmark counterparts, which highlights the that RIS can effectively enhance the uplink anti-jamming WIT capability as well as the downlink WPT efficiency.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4622-4637"},"PeriodicalIF":6.3,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866762","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":"GIFDL: Generated Image Fluctuation Distortion Learning for Enhancing Steganographic Security","authors":"Xiangkun Wang;Kejiang Chen;Yuang Qi;Ruiheng Liu;Weiming Zhang;Nenghai Yu","doi":"10.1109/TIFS.2025.3563817","DOIUrl":"10.1109/TIFS.2025.3563817","url":null,"abstract":"Minimum distortion steganography is currently the mainstream method for modification-based steganography. A key issue in this method is how to define steganographic distortion. With the rapid development of deep learning technology, the definition of distortion has evolved from manual design to deep learning design. Concurrently, rapid advancements in image generation have made generated images viable as cover media. However, existing distortion design methods based on machine learning do not fully leverage the advantages of generated cover media, resulting in suboptimal security performance. To address this issue, we propose GIFDL (Generated Image Fluctuation Distortion Learning), a steganographic distortion learning method based on the fluctuations in generated images. Inspired by the idea of natural steganography, we take a series of highly similar fluctuation images as the input to the steganographic distortion generator and introduce a new GAN training strategy to disguise stego images as fluctuation images. Experimental results demonstrate that GIFDL, compared with state-of-the-art GAN-based distortion learning methods, exhibits superior resistance to steganalysis, increasing the detection error rates by an average of 3.30% across three steganalysis.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4581-4594"},"PeriodicalIF":6.3,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866788","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":"Improving Transferable Targeted Adversarial Attack via Normalized Logit Calibration and Truncated Feature Mixing","authors":"Juanjuan Weng;Zhiming Luo;Shaozi Li","doi":"10.1109/TIFS.2025.3563820","DOIUrl":"10.1109/TIFS.2025.3563820","url":null,"abstract":"This paper aims to enhance the transferability of adversarial samples in targeted attacks, where attack success rates remain comparatively low. To achieve this objective, we propose two distinct techniques for improving the targeted transferability from the loss and feature aspects. First, in previous approaches, logit calibrations used in targeted attacks primarily focus on the logit margin between the targeted class and the untargeted classes among samples, neglecting the standard deviation of the logit. In contrast, we introduce a new normalized logit calibration method that jointly considers the logit margin and the standard deviation of logits. This approach effectively calibrates the logits, enhancing the targeted transferability. Second, previous studies have demonstrated that mixing the features of clean samples during optimization can significantly increase transferability. Building upon this, we further investigate a truncated feature mixing method to reduce the impact of the source training model, resulting in additional improvements. The truncated feature is determined by removing the Rank-1 feature associated with the largest singular value decomposed from the high-level convolutional layers of the clean sample. Extensive experiments conducted on the ImageNet-Compatible, CIFAR-10 and ImageNet-1k datasets demonstrate the individual and mutual benefits of our proposed two components, which outperform the state-of-the-art methods by a large margin in black-box targeted attacks.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4595-4609"},"PeriodicalIF":6.3,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866763","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":"Yesterday Once MorE: Facilitating Linux Kernel Bug Reproduction via Reverse Fuzzing","authors":"Xingwei Li, Yan Kang, Chenggang Wu, Danjun Liu, Jiming Wang, Yue Sun, Zehui Wu, Yunchao Wang, Rongkuan Ma, Qiang Wei","doi":"10.1109/tifs.2025.3562704","DOIUrl":"https://doi.org/10.1109/tifs.2025.3562704","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"46 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862331","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":"Unveiling the Superiority of Unsupervised Learning on GPU Cryptojacking Detection: Practice on Magnetic Side Channel-Based Mechanism","authors":"Hangcheng Cao;Guowen Xu;Ziyang He;Shaoqing Shi;Shengmin Xu;Cong Wu;Jianting Ning","doi":"10.1109/TIFS.2025.3563069","DOIUrl":"10.1109/TIFS.2025.3563069","url":null,"abstract":"Ample profits of GPU cryptojacking attract hackers to recklessly invade victims’ devices, for completing specific cryptocurrency mining tasks. Such malicious invasion undoubtedly obstructs normal device usage and wastes computation resources. To resist the threat of GPU cryptojacking, existing works aim to timely detect and clear away it, by distinguishing the dissimilitude between it and legitimate applications. However, these detection mechanisms inappropriately rely on two conflict cornerstones, manifested in leveraging mutable samples of illegitimate cryptojacking to design supervision-based detection models requiring samples with stable patterns. This limitation compromises the practicability of existing detection mechanisms in the face of mutable cryptojacking samples. To fill the gap, we explore the superiority of unsupervised learning in handling this issue and further propose an unsupervised manner-enabled detection mechanism named MagInspector, only using legitimate applications’ magnetic signatures from GPU side channels for model construction. MagInspector innovates in training an unsupervised autoencoder network by an adversarial mode that well learns the stable signature patterns of legitimate applications, while incompatible with mutable cryptojacking ones. In the process of model training, we elaborately extract mutual energy cumulation distribution features to represent legitimate applications to overcome the impact of their inter-type differences. Meanwhile, a locality sensitive hashing-driven outlier removal algorithm is designed to enhance MagInspector’s robustness to the noise samples. Finally, extensive experiments are conducted on GPUs covering four generations of common NVIDIA architectures and two generations of AMD architectures; the results show that applying MagInspector to mutable cryptojacking signature detection achieves a significant average accuracy improvement of 25.5% and 17.8%, respectively.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4874-4889"},"PeriodicalIF":6.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857661","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":"ColorVein: Colorful Cancelable Vein Biometrics","authors":"Yifan Wang;Jie Gui;Xinli Shi;Linqing Gui;Yuan Yan Tang;James Tin-Yau Kwok","doi":"10.1109/TIFS.2025.3562690","DOIUrl":"10.1109/TIFS.2025.3562690","url":null,"abstract":"Vein recognition technologies have become one of the primary solutions for high-security identification systems. However, the issue of biometric information leakage can still pose a serious threat to user privacy and anonymity. Currently, there is no cancelable biometric template generation scheme specifically designed for vein biometrics. Therefore, this paper proposes an innovative cancelable vein biometric generation scheme: ColorVein. Unlike previous cancelable template generation schemes, ColorVein does not destroy the original biometric features and introduces additional color information to grayscale vein images. This method significantly enhances the information density of vein images by transforming static grayscale information into dynamically controllable color representations through interactive colorization. ColorVein allows users/administrators to define a controllable pseudo-random color space for grayscale vein images by editing the position, number, and color of hint points, thereby generating protected cancelable templates. Additionally, we propose a new secure center loss to optimize the training process of the protected feature extraction model, effectively increasing the feature distance between enrolled users and any potential impostors. Finally, we evaluate ColorVein’s performance on all types of vein biometrics, including recognition performance, unlinkability, irreversibility, and revocability, and conduct security and privacy analyses. ColorVein achieves competitive performance compared with state-of-the-art methods.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4943-4955"},"PeriodicalIF":6.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857662","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":"More Efficient, Privacy-Enhanced, and Powerful Privacy-Preserving Feature Retrieval Private Set Intersection","authors":"Guowei Ling;Peng Tang;Jinyong Shan;Fei Tang;Weidong Qiu","doi":"10.1109/TIFS.2025.3562695","DOIUrl":"10.1109/TIFS.2025.3562695","url":null,"abstract":"Private Set Intersection (PSI) allows two parties, the sender and the receiver, each possessing a private set, to compute the intersection of their sets, with only the receiver learning the intersection and without revealing any additional information. Privacy-Preserving Feature Retrieval PSI (<inline-formula> <tex-math>$mathsf {P^{2}FRPSI}$ </tex-math></inline-formula>) is a variant of PSI. In <inline-formula> <tex-math>$mathsf {P^{2}FRPSI}$ </tex-math></inline-formula>, the receiver designs a predicate and obtains the intersection of private sets that satisfy this predicate, while the sender learns nothing about the predicate. However, the existing two <inline-formula> <tex-math>$textsf {PRFPSI}$ </tex-math></inline-formula> protocols (<inline-formula> <tex-math>$textsf {TIFS 2024}$ </tex-math></inline-formula>), based respectively on the DH key agreement and Oblivious Pseudo-Random Function (OPRF), are not highly efficient due to their reliance on expensive homomorphic encryption. Moreover, the existing DH-based <inline-formula> <tex-math>$mathsf {P^{2}FRPSI}$ </tex-math></inline-formula> protocol reveals the output size and the original intersection size to the sender. We also observed that the existing <inline-formula> <tex-math>$mathsf {P^{2}FRPSI}$ </tex-math></inline-formula> protocols do not support threshold retrieval and the logical connective <inline-formula> <tex-math>$textsf {OR}$ </tex-math></inline-formula> and can only work when feature values of the sender have very low dimensionality. This paper also proposes two new <inline-formula> <tex-math>$mathsf {P^{2}FRPSI}$ </tex-math></inline-formula> protocols, one based on DH key agreement and the other based on OPRF, to fully address the issues present in existing <inline-formula> <tex-math>$mathsf {P^{2}FRPSI}$ </tex-math></inline-formula> protocols. Our DH-based <inline-formula> <tex-math>$mathsf {P^{2}FRPSI}$ </tex-math></inline-formula> is <inline-formula> <tex-math>$30 times $ </tex-math></inline-formula> faster than the existing DH-based protocol, with only a 36% increase in communication overhead. Furthermore, our OPRF-based <inline-formula> <tex-math>$mathsf {P^{2}FRPSI}$ </tex-math></inline-formula> protocol is <inline-formula> <tex-math>$2 times $ </tex-math></inline-formula> as fast as existing OPRF-based protocol and reduces communication overhead by a factor of 4.6. Our DH-based <inline-formula> <tex-math>$mathsf {P^{2}FRPSI}$ </tex-math></inline-formula> protocol completely eliminates the leakage of the original intersection size and the output size. Meanwhile, our protocols support the logical connective <inline-formula> <tex-math>$textsf {OR}$ </tex-math></inline-formula> for linking sub-predicates and also enable threshold-based retrieval. They are proven to be secure in the semi-honest model. Our open-source implementations can be found at <uri>https://github.com/ShallMate/pfrpsi</uri>, which can help readers understand our protocols and reproduce the experiments.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4815-4827"},"PeriodicalIF":6.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858067","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":"An Enhanced Linearly Homomorphic Network Coding Signature Scheme for Secure Data Delivery in IoT Networks","authors":"Hao Huang, Xiaofen Wang, Man Ho Au, Sheng Cao, Qinglin Zhao, Jiguo Yu","doi":"10.1109/tifs.2025.3563074","DOIUrl":"https://doi.org/10.1109/tifs.2025.3563074","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"113 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858065","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":"A Random-Binding-Based Bio-Hashing Template Protection Method for Palm Vein Recognition","authors":"Tianming Xie;Wenxiong Kang","doi":"10.1109/TIFS.2025.3559791","DOIUrl":"10.1109/TIFS.2025.3559791","url":null,"abstract":"To mitigate the risk of data breaches, an increasing number of biometric recognition systems are introducing encryption biometric template protection methods and directly matching in the encrypted domain. Depending on the approach to key management, prevailing biometric template protection strategies can be categorized into declarative and distributive methods. The former are challenged by complexities and vulnerabilities linked to key loss, while the latter are compromised by fixed mapping rules that may expose personal information. We present a biometric template protection method that combines random-fixed factors to handle these challenges, thereby protecting the user’s biometric privacy. Firstly, we introduce a random activation factor generation module that extracts scaling and offset factors from the user’s biometric data. This module randomly binds factors to different positions in each authentication process, rendering distance-dependent bitwise cracking algorithms ineffective. Secondly, we propose a fixed multi-branch mapping module that enhances feature expression and minimizes information loss post-encryption. We also develop a trainable min-max hash method, optimized using an improved approximate contrastive loss. Employing palm veins as a case study, we conducted experiments across five datasets, where our method outperformed other encrypted domain methods and showed competitive advantages over mainstream non-encrypted methods. Moreover, we have demonstrated that our method ensures robust performance while meeting essential security requirements of irreversibility, unlinkability, and revocability.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4243-4255"},"PeriodicalIF":6.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858066","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":"DC-SGD: Differentially Private SGD With Dynamic Clipping Through Gradient Norm Distribution Estimation","authors":"Chengkun Wei;Weixian Li;Chen Gong;Wenzhi Chen","doi":"10.1109/TIFS.2025.3557755","DOIUrl":"10.1109/TIFS.2025.3557755","url":null,"abstract":"Differentially Private Stochastic Gradient Descent (DP-SGD) is a widely adopted technique for privacy-preserving deep learning. A critical challenge in DP-SGD is selecting the optimal clipping threshold C, which involves balancing the trade-off between clipping bias and noise magnitude, incurring substantial privacy and computing overhead during hyperparameter tuning. In this paper, we propose Dynamic Clipping DP-SGD (DC-SGD), a framework that leverages differentially private histograms to estimate gradient norm distributions and dynamically adjust the clipping threshold C. Our framework includes two novel mechanisms: DC-SGD-P and DC-SGD-E. DC-SGD-P adjusts the clipping threshold based on a percentile of gradient norms, while DC-SGD-E minimizes the expected squared error of gradients to optimize C. These dynamic adjustments significantly reduce the burden of hyperparameter tuning C. The extensive experiments on various deep learning tasks, including image classification and natural language processing, show that our proposed dynamic algorithms achieve up to 9 times acceleration on hyperparameter tuning than DP-SGD. And DC-SGD-E can achieve an accuracy improvement of 10.62% on CIFAR10 than DP-SGD under the same privacy budget of hyperparameter tuning. We conduct rigorous theoretical privacy and convergence analyses, showing that our methods seamlessly integrate with the Adam optimizer. Our results highlight the robust performance and efficiency of DC-SGD, offering a practical solution for differentially private deep learning with reduced computational overhead and enhanced privacy guarantees.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4498-4511"},"PeriodicalIF":6.3,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849784","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}