IEEE Transactions on Information Forensics and Security最新文献_第6页

BAST: Blockchain-Assisted Secure and Traceable Data Sharing Scheme for Vehicular Networks BAST：区块链辅助的车辆网络安全可追溯数据共享方案

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-29 DOI: 10.1109/TIFS.2025.3565372

Xinzhong Liu;Jie Cui;Jing Zhang;Rongwang Yin;Hong Zhong;Lu Wei;Irina Bolodurina;Debiao He

{"title":"BAST: Blockchain-Assisted Secure and Traceable Data Sharing Scheme for Vehicular Networks","authors":"Xinzhong Liu;Jie Cui;Jing Zhang;Rongwang Yin;Hong Zhong;Lu Wei;Irina Bolodurina;Debiao He","doi":"10.1109/TIFS.2025.3565372","DOIUrl":"10.1109/TIFS.2025.3565372","url":null,"abstract":"In vehicular networks, caching service content on edge servers (ESs) is a widely accepted strategy for promptly responding to vehicle requests, reducing communication overhead, and improving service experience. However, implementing such an architecture requires addressing the challenges associated with ES response data reliability and communication security. In this study, to tackle the ES response data reliability issue, a blockchain-assisted threshold signature scheme for cache-based vehicular networks is proposed. The scheme utilizes a threshold mechanism to sign the data broadcast by the ES, incorporates blockchain to trace malicious signers, and avoids the shortcomings and limitations associated with idealized assumptions for the ES in existing data-sharing schemes. Moreover, considering the communication security and high-speed mobility of vehicles, using the non-interactive signatures of knowledge based on the <inline-formula> <tex-math>$Sigma $ </tex-math></inline-formula>-protocol, a secure and efficient message authentication scheme for vehicles and ESs is provided. Through rigorous security proofs and comprehensive analyses, our scheme satisfies the communication security requirements of vehicular networks. By leveraging the JPBC library for performance analysis, the proposed scheme demonstrates advantages as concerns both computation and communication overheads compared to related schemes. Moreover, we implemented the proposed scheme on an Ethereum test network (i.e., Goerli) to validate its feasibility.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4664-4678"},"PeriodicalIF":6.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889756","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}

引用次数: 0

Threshold Password-Hardening Updatable Oblivious Key Management 阈值密码加固可更新遗忘密钥管理

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-29 DOI: 10.1109/TIFS.2025.3565371

Changsong Jiang;Chunxiang Xu;Zhen Liu;Xinfeng Dong;Wenzheng Zhang

{"title":"Threshold Password-Hardening Updatable Oblivious Key Management","authors":"Changsong Jiang;Chunxiang Xu;Zhen Liu;Xinfeng Dong;Wenzheng Zhang","doi":"10.1109/TIFS.2025.3565371","DOIUrl":"10.1109/TIFS.2025.3565371","url":null,"abstract":"We propose a threshold password-hardening updatable oblivious key management system dubbed TPH-UOKM for cloud storage. In TPH-UOKM, a group of key servers share a user-specific secret key for a user, and assist the user in producing her/his password-derived private key in a threshold and oblivious way, where the password is hardened to resist offline dictionary guessing attacks. Anyone can outsource data protected with the user’s password-derived public key to the cloud server, and merely the user holding the correct password can recover the password-derived private key for data access. TPH-UOKM can accomplish decryption of N ciphertexts with the complexity <inline-formula> <tex-math>$O(1)$ </tex-math></inline-formula> of communication between a user and the key servers, which outperforms existing schemes. TPH-UOKM supports password update. The cloud server can update all protected data of a user with an update token to be accessible only with the new password, which resists password leakage. We present a two-level proactivization mechanism to periodically update user-specific secret key shares and the key servers to thwart perpetual compromise of them, where the renewal of user-specific secret key shares reduces computation and communication costs compared to existing approaches. Provable security and high efficiency of TPH-UOKM are demonstrated by comprehensive analyses and performance evaluations.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4799-4814"},"PeriodicalIF":6.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889755","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}

引用次数: 0

Across-Platform Detection of Malicious Cryptocurrency Accounts via Interaction Feature Learning 基于交互特征学习的恶意加密货币账户跨平台检测

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-28 DOI: 10.1109/TIFS.2025.3565130

Zheng Che;Meng Shen;Zhehui Tan;Hanbiao Du;Wei Wang;Ting Chen;Qinglin Zhao;Yong Xie;Liehuang Zhu

{"title":"Across-Platform Detection of Malicious Cryptocurrency Accounts via Interaction Feature Learning","authors":"Zheng Che;Meng Shen;Zhehui Tan;Hanbiao Du;Wei Wang;Ting Chen;Qinglin Zhao;Yong Xie;Liehuang Zhu","doi":"10.1109/TIFS.2025.3565130","DOIUrl":"10.1109/TIFS.2025.3565130","url":null,"abstract":"With the rapid evolution of Web3.0, cryptocurrency has become a cornerstone of decentralized finance. While these digital assets enable efficient and borderless financial transactions, their pseudonymous nature has also attracted malicious activities such as money laundering, fraud, and other financial crimes. Effective detection of malicious accounts is crucial to maintaining the security and integrity of the Web 3.0 ecosystem. Existing malicious account detection methods rely on large amounts of labeled data and suffer from low generalization. Label-efficient and generalizable malicious account detection remains a challenging task. In this paper, we propose ShadowEyes, a framework for detecting malicious accounts by leveraging interaction feature learning with only a small labeled dataset. Specifically, We first propose a generalized account representation named TxGraph, which captures the universal interaction features of Ethereum and Bitcoin. Then we carefully design an account representation augmentation method tailored to simulate the evolution of malicious accounts to generate positive pairs. We conduct extensive experiments using public datasets to evaluate the performance of ShadowEyes. The results demonstrate that it outperforms state-of-the-art (SOTA) methods in four typical scenarios. Specifically, in the scenario of across-platform malicious account detection, ShadowEyes maintains an F1 score of around 90%, which is 10% higher than the SOTA method. In the zero-shot learning scenario, it can achieve an F1 score of 79.56% for detecting gambling accounts, surpassing the SOTA method by 10.44%.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4783-4798"},"PeriodicalIF":6.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884822","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}

引用次数: 0

RandFlash: Breaking the Quadratic Barrier in Large-Scale Distributed Randomness Beacons RandFlash：打破大规模分布式随机信标的二次障碍

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-28 DOI: 10.1109/TIFS.2025.3564877

Yang Yang;Bingyu Li;Qianhong Wu;Bo Qin;Qin Wang;Shihong Xiong;Willy Susilo

{"title":"RandFlash: Breaking the Quadratic Barrier in Large-Scale Distributed Randomness Beacons","authors":"Yang Yang;Bingyu Li;Qianhong Wu;Bo Qin;Qin Wang;Shihong Xiong;Willy Susilo","doi":"10.1109/TIFS.2025.3564877","DOIUrl":"10.1109/TIFS.2025.3564877","url":null,"abstract":"Random beacons are of paramount importance in distributed systems (e.g., blockchain, electronic voting, governance). The sheer scale of nodes inherent in distributed environments necessitates minimizing communication overhead per node while ensuring protocol availability, particularly under adversarial conditions. Existing solutions have managed to reduce the optimistic overhead to a minimum of <inline-formula> <tex-math>$O(n^{2})$ </tex-math></inline-formula>, where n represents the node count of the system. In this paper, we step further by proposing and implementing RandFlash, a leaderless random beacon protocol that achieves an optimistic communication complexity of <inline-formula> <tex-math>$O(nlog n)$ </tex-math></inline-formula>. Evaluation results demonstrate that RandFlash outperforms existing constructions, RandPiper (CCS’21) and OptRand (NDSS’23), in terms of the number of random beacons generated within large-scale networks comprising 64 nodes or more (e.g., in sizes of 80 and 128). Furthermore, RandFlash exhibits resilience, capable of withstanding up to one-third of the nodes acting maliciously, all without the need for strongly trusted setups (i.e., embedding a secret trapdoor by trusted third parties). We also provide formal security proofs validating all properties upheld by this lineage.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4710-4725"},"PeriodicalIF":6.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884830","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}

引用次数: 0

Confidence Aware Learning for Reliable Face Anti-Spoofing 可靠人脸防欺骗的自信感知学习

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-28 DOI: 10.1109/TIFS.2025.3564878

Xingming Long;Jie Zhang;Shiguang Shan

{"title":"Confidence Aware Learning for Reliable Face Anti-Spoofing","authors":"Xingming Long;Jie Zhang;Shiguang Shan","doi":"10.1109/TIFS.2025.3564878","DOIUrl":"10.1109/TIFS.2025.3564878","url":null,"abstract":"Current Face Anti-spoofing (FAS) models tend to make overly confident predictions even when encountering unfamiliar scenarios or unknown presentation attacks, which leads to serious potential risks. To solve this problem, we propose a Confidence Aware Face Anti-spoofing (CA-FAS) model, which is aware of its capability boundary, thus achieving reliable liveness detection within this boundary. To enable the CA-FAS to “know what it doesn’t know”, we propose to estimate its confidence during the prediction of each sample. Specifically, we build Gaussian distributions for both the live faces and the known attacks. The prediction confidence for each sample is subsequently assessed using the Mahalanobis distance between the sample and the Gaussians for the “known data”. We further introduce the Mahalanobis distance-based triplet mining to optimize the parameters of both the model and the constructed Gaussians as a whole. Extensive experiments show that the proposed CA-FAS can effectively recognize samples with low prediction confidence and thus achieve much more reliable performance than other FAS models by filtering out samples that are beyond its reliable range.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"5083-5093"},"PeriodicalIF":6.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884873","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}

引用次数: 0

Distributional Black-Box Model Inversion Attack with Multi-Agent Reinforcement Learning 基于多智能体强化学习的分布式黑盒模型反演攻击

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-24 DOI: 10.1109/tifs.2025.3564043

Huan Bao, Kaimin Wei, Yongdong Wu, Jin Qian, Robert H. Deng

引用次数: 0

Stealthy Attacks With Historical Data on Distributed State Estimation 基于分布式状态估计的历史数据隐身攻击

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-24 DOI: 10.1109/TIFS.2025.3564063

Jitao Xing;Dan Ye;Pengyu Li

引用次数: 0

Loop-Back Mechanism-Based Physical-Layer Secret Key Generation in FDD System Under Hardware Impairments 硬件缺陷下基于环回机制的FDD系统物理层密钥生成

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-24 DOI: 10.1109/TIFS.2025.3564064

Xuan Yang;Dongming Li

{"title":"Loop-Back Mechanism-Based Physical-Layer Secret Key Generation in FDD System Under Hardware Impairments","authors":"Xuan Yang;Dongming Li","doi":"10.1109/TIFS.2025.3564064","DOIUrl":"10.1109/TIFS.2025.3564064","url":null,"abstract":"In physical-layer secret key generation, key generation performance is susceptible to hardware impairments (HIs), which can degrade channel reciprocity. Additionally, the security of loop-back mechanism based schemes in frequency division duplex (FDD) systems requires further enhancement. To address these challenges, this paper proposes a secure key generation scheme based on a loop-back mechanism for FDD systems. By transmitting signals across different frequency bands in a loop-back fashion, the proposed scheme mitigates the adverse effects of HI variations across frequency bands and enhances system security. Theoretical analyses are conducted on the normalized mean square error and the secret key rate of the loop-back mechanism in both FDD and time division duplex (TDD) systems, providing a clear security assessment of the proposed scheme. Simulation and experimental results demonstrate that by accounting for HI differences in the frequency domain, the proposed scheme improves channel reciprocity, enhances the secret key rate, achieves a higher key generation rate (KGR), and reduces the key disagreement ratio (KDR) compared to state-of-the-art methods.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4694-4709"},"PeriodicalIF":6.3,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873003","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}

引用次数: 0

Throughput Improvement for RIS-Empowered Wireless Powered Anti-Jamming Communication Networks (WPAJCN) 基于ris的无线供电抗干扰通信网络（WPAJCN）吞吐量改进

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-23 DOI: 10.1109/TIFS.2025.3563818

Zheng Chu;David Chieng;Chiew Foong Kwong;Huan Jin;Zhengyu Zhu;Chongwen Huang;Chau Yuen

{"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}

引用次数: 0

GIFDL: Generated Image Fluctuation Distortion Learning for Enhancing Steganographic Security GIFDL：用于增强隐写安全性的生成图像波动失真学习

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-23 DOI: 10.1109/TIFS.2025.3563817

Xiangkun Wang;Kejiang Chen;Yuang Qi;Ruiheng Liu;Weiming Zhang;Nenghai Yu

{"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}

引用次数: 0