IEEE Transactions on Information Forensics and Security最新文献_第3页

Secure Service Function Chain Provisioning for Task Offloading in Device-Edge-Cloud Computing 为设备边缘云计算中的任务卸载提供安全服务功能链

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-22 DOI: 10.1109/tifs.2025.3553013

Jianhua Liu, Xin Wang, Kui Ren, Yiyi Zhou, Minglu Li

引用次数: 0

Intellectual Property Protection for Deep Models: Pioneering Cross-Domain Fingerprinting Solutions

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-22 DOI: 10.1109/tifs.2025.3552175

Tianhua Xu, Sheng-hua Zhong, Zhi Zhang, Yan Liu

引用次数: 0

DDL: Effective and Comprehensible Interpretation Framework for Diverse Deepfake Detectors DDL：适用于多种深度伪造检测器的有效且可理解的解释框架

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-21 DOI: 10.1109/TIFS.2025.3553803

Zekun Sun;Na Ruan;Jianhua Li

{"title":"DDL: Effective and Comprehensible Interpretation Framework for Diverse Deepfake Detectors","authors":"Zekun Sun;Na Ruan;Jianhua Li","doi":"10.1109/TIFS.2025.3553803","DOIUrl":"10.1109/TIFS.2025.3553803","url":null,"abstract":"In the context of escalating advancements in AI generative technologies, Deepfakes, the sophisticated face forgeries created using deep learning methods, have emerged as a significant security threat. The predominant countermeasures are Deepfake detectors based on deep learning (DL). However, due to the opaque nature of DL-model, they struggle to offer understandable explanations for their predictive decisions, which undermines their reliability and effectiveness in real-world applications. Existing mainstream DL-oriented interpretation approaches, the feature attribution methods, struggle to work on Deepfake detectors due to issues of low interpretation fidelity, poor intelligibility, and limited applicability across different types of detectors. This paper addresses these critical challenges by proposing the Deepfake Detector Lens (<monospace>DDL</monospace>), a novel framework designed to enhance the interpretability of diverse architectural Deepfake detectors, encompassing those based on image, frequency domain, and video. <monospace>DDL</monospace> employs a heuristic algorithm to enhance interpretation efficacy and incorporates image segmentation and face parsing techniques to bridge the gap between the machine-generated interpretation saliency map and human understanding. Comprehensive evaluations of <monospace>DDL</monospace> demonstrate its superiority over existing feature attribution methods in terms of fidelity, intelligibility, and applicability. The proposed <monospace>DDL</monospace> significantly advances the interpretability of Deepfake detection technology, offering a more reliable and understandable tool for combating AI-generated face forgeries.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"3601-3615"},"PeriodicalIF":6.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672481","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

Driving State-aware Anomaly Detection for Autonomous Vehicles 自动驾驶汽车的驾驶状态感知异常检测

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-21 DOI: 10.1109/tifs.2025.3553370

Zhihao Wang, Lei Xue, Xiapu Luo, Xiaobo Ma, Guofei Gu

引用次数: 0

Complete Coherent Demodulation and Recovery of Spread Spectrum Clocking-based Electromagnetic Information Leakage: Theory and Demonstration

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-19 DOI: 10.1109/tifs.2025.3553029

Euibum Lee, Dong-Hoon Choi, Taesik Nam, Inhwan Kim, Youngjae Yu, Jong-Gwan Yook

引用次数: 0

On the Vulnerability of Retrieval in High Intrinsic Dimensionality Neighborhood

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-19 DOI: 10.1109/TIFS.2025.3553067

Teddy Furon

引用次数: 0

Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-19 DOI: 10.1109/tifs.2025.3553043

Yi Yu, Song Xia, Xun Lin, Chenqi Kong, Wenhan Yang, Shijian Lu, Yap-Peng Tan, Alex C. Kot

引用次数: 0

Mutual Information Guided Backdoor Mitigation for Pre-Trained Encoders 针对预训练编码器的互信息引导后门缓解技术

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-18 DOI: 10.1109/TIFS.2025.3550062

Tingxu Han;Weisong Sun;Ziqi Ding;Chunrong Fang;Hanwei Qian;Jiaxun Li;Zhenyu Chen;Xiangyu Zhang

{"title":"Mutual Information Guided Backdoor Mitigation for Pre-Trained Encoders","authors":"Tingxu Han;Weisong Sun;Ziqi Ding;Chunrong Fang;Hanwei Qian;Jiaxun Li;Zhenyu Chen;Xiangyu Zhang","doi":"10.1109/TIFS.2025.3550062","DOIUrl":"10.1109/TIFS.2025.3550062","url":null,"abstract":"Self-supervised learning (SSL) is increasingly attractive for pre-training encoders without requiring labeled data. Downstream tasks built on top of those pre-trained encoders can achieve nearly state-of-the-art performance. The pre-trained encoders by SSL, however, are vulnerable to backdoor attacks as demonstrated by existing studies. Numerous backdoor mitigation techniques are designed for downstream task models. However, their effectiveness is impaired and limited when adapted to pre-trained encoders, due to the lack of label information when pre-training. To address backdoor attacks against pre-trained encoders, in this paper, we innovatively propose a mutual information guided backdoor mitigation technique, named MIMIC(<underline>M</u>utual <underline>I</u>nformation guided backdoor <underline>MI</u>tigation for pre-trained en<underline>C</u>oders). MIMIC uses the potentially backdoored encoder as the teacher network and applies knowledge distillation to create a clean student encoder from it. Different from existing knowledge distillation approaches, MIMIC initializes the student with random weights, inheriting no backdoors from teacher nets. Then MIMIC leverages mutual information between each layer and extracted features to locate where benign knowledge lies in the teacher net, with which distillation is deployed to clone clean features from teacher to student. We craft the distillation loss with two aspects, including clone loss and attention loss, aiming to mitigate backdoors and maintain encoder performance at the same time. Our evaluation conducted on two backdoor attacks in SSL demonstrates that MIMIC can significantly reduce the attack success rate by only utilizing <inline-formula> <tex-math>$leq 5$ </tex-math></inline-formula>% of clean pre-training data that is accessible to the defender, surpassing seven state-of-the-art backdoor mitigation techniques. The source code of MIMIC is available at <uri>https://github.com/wssun/MIMIC</uri>.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"3414-3428"},"PeriodicalIF":6.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661203","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

Privacy-Preserving Vertical Federated Learning With Tensor Decomposition for Data Missing Features

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-18 DOI: 10.1109/TIFS.2025.3552033

Tianchi Liao;Lele Fu;Lei Zhang;Lei Yang;Chuan Chen;Michael K. Ng;Huawei Huang;Zibin Zheng

{"title":"Privacy-Preserving Vertical Federated Learning With Tensor Decomposition for Data Missing Features","authors":"Tianchi Liao;Lele Fu;Lei Zhang;Lei Yang;Chuan Chen;Michael K. Ng;Huawei Huang;Zibin Zheng","doi":"10.1109/TIFS.2025.3552033","DOIUrl":"10.1109/TIFS.2025.3552033","url":null,"abstract":"Vertical federated learning (VFL) allows parties to build robust shared machine learning models based on learning from distributed features of the same samples, without exposing their own data. However, current VFL solutions are limited in their ability to perform inference on non-overlapping samples, and data stored on clients is often subject to loss due to various unavoidable factors. This leads to incomplete client data, where client missing features (MF) are frequently overlooked in VFL. The main aim of this paper is to propose a VFL framework to handle missing features (MFVFL), which is a tensor decomposition network-based approach that can effectively learn intra- and inter-client feature information from client data with missing features to improve VFL performance. In the proposed MFVFL method each client imputes missing values and encodes features to learn intra-feature information, and the server collects the uploaded feature embeddings as input to our developed low-rank tensor decomposition network to learn inter-feature information. Finally, the server aggregates the representations from tensor decomposition to train a global classifier. In the paper, we theoretically guarantee the convergence of MFVFL. In addition, differential privacy (DP) for data privacy protection is always used, and the proposed framework (MFVFL-DP) can deal with such degraded data by using a tensor robust PCA to alleviate the impact of noise while preserving data privacy. We conduct extensive experiments on six datasets of different sample sizes and feature dimensions, and demonstrate that MFVFL significantly outperforms state-of-the-art methods, especially under high missing ratios. The experimental results also show that MFVFL-DP possesses excellent denoising capabilities and illustrate that the noisy effect by the DP mechanism can be alleviated.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"3445-3460"},"PeriodicalIF":6.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661411","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

From Σ-Protocol-Based Signatures to Ring Signatures: General Construction and Applications 从基于Σ协议的签名到环形签名：一般构造和应用

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-17 DOI: 10.1109/TIFS.2025.3552199

Xue Chen;Shang Gao;Shiyuan Xu;Liquan Chen;Siu-Ming Yiu;Bin Xiao

{"title":"From Σ-Protocol-Based Signatures to Ring Signatures: General Construction and Applications","authors":"Xue Chen;Shang Gao;Shiyuan Xu;Liquan Chen;Siu-Ming Yiu;Bin Xiao","doi":"10.1109/TIFS.2025.3552199","DOIUrl":"10.1109/TIFS.2025.3552199","url":null,"abstract":"Public Key Infrastructure (PKI) has gained widespread attention for ensuring the security and integrity of data communication. While existing PKI mainly supports digital signatures, it is lacking in crucial anonymity, leading to the leakage of a signer’s identity information. To alleviate the issue, ring signatures are a suitable choice to provide anonymity as they allow users to create their own rings without the need for an administrator. Unfortunately, the utilization of ring signatures in PKI may present compatibility challenges within the system. Thus, proposing a general mechanism to convert a standardized <inline-formula> <tex-math>$Sigma $ </tex-math></inline-formula>-based signature to a ring signature is far-reaching. In this paper, we propose a general construction for converting <inline-formula> <tex-math>$Sigma $ </tex-math></inline-formula>-based signatures into ring signatures. To achieve this, we first introduce a <inline-formula> <tex-math>$Sigma $ </tex-math></inline-formula>-based general model, providing a general transformation to convert existing <inline-formula> <tex-math>$Sigma $ </tex-math></inline-formula>-based signatures into a <inline-formula> <tex-math>$Sigma $ </tex-math></inline-formula>-protocol form. Subsequently, we incorporate our redesigned one-out-of-many relation within our general model and proceed to devise ring signatures leveraging on one-out-of-many proofs. Furthermore, to reduce the signature size, we employ the Bulletproofs folding technique, enabling the attainment of logarithmic size ring signatures. To demonstrate the wide applicability of our general construction, we present four prominent signatures as case studies. Ultimately, we conduct a rigorous security analysis and benchmark experimental evaluation. The signing and verification times are 0.44 to 0.97 times and 0.27 to 0.91 times compared to other state-of-the-art schemes, respectively. Additionally, we exhibit the lowest signature size to date.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"3646-3661"},"PeriodicalIF":6.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640676","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