IEEE Transactions on Information Forensics and Security最新文献_第4页

Efficient and Secure Post-Quantum Certificateless Signcryption with Linkability for IoMT

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-18 DOI: 10.1109/tifs.2024.3520007

Shiyuan Xu, Xue Chen, Yu Guo, Siu-Ming Yiu, Shang Gao, Bin Xiao

引用次数: 0

GroupFace: Imbalanced Age Estimation Based on Multi-Hop Attention Graph Convolutional Network and Group-Aware Margin Optimization GroupFace：基于多跳注意力图卷积网络和群体感知边际优化的不平衡年龄估计

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-18 DOI: 10.1109/TIFS.2024.3520020

Yiping Zhang;Yuntao Shou;Wei Ai;Tao Meng;Keqin Li

{"title":"GroupFace: Imbalanced Age Estimation Based on Multi-Hop Attention Graph Convolutional Network and Group-Aware Margin Optimization","authors":"Yiping Zhang;Yuntao Shou;Wei Ai;Tao Meng;Keqin Li","doi":"10.1109/TIFS.2024.3520020","DOIUrl":"10.1109/TIFS.2024.3520020","url":null,"abstract":"With the recent advances in computer vision, age estimation has significantly improved in overall accuracy. However, owing to the most common methods do not take into account the class imbalance problem in age estimation datasets, they suffer from a large bias in recognizing long-tailed groups. To achieve high-quality imbalanced learning in long-tailed groups, the dominant solution lies in that the feature extractor learns the discriminative features of different groups and the classifier is able to provide appropriate and unbiased margins for different groups by the discriminative features. Therefore, in this novel, we propose an innovative collaborative learning framework (GroupFace) that integrates a multi-hop attention graph convolutional network and a dynamic group-aware margin strategy based on reinforcement learning. Specifically, to extract the discriminative features of different groups, we design an enhanced multi-hop attention graph convolutional network. This network is capable of capturing the interactions of neighboring nodes at different distances, fusing local and global information to model facial deep aging, and exploring diverse representations of different groups. In addition, to further address the class imbalance problem, we design a dynamic group-aware margin strategy based on reinforcement learning to provide appropriate and unbiased margins for different groups. The strategy divides the sample into four age groups and considers identifying the optimum margins for various age groups by employing a Markov decision process. Under the guidance of the agent, the feature representation bias and the classification margin deviation between different groups can be reduced simultaneously, balancing inter-class separability and intra-class proximity. After joint optimization, our architecture achieves excellent performance on several age estimation benchmark datasets. It not only achieves large improvements in overall estimation accuracy but also gains balanced performance in long-tailed group estimation.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"605-619"},"PeriodicalIF":6.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849469","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 Localization for Underwater Acoustic Sensor Networks: A Differential Privacy-Based Deep Learning Approach 水下声学传感器网络的隐私保护定位：基于差异隐私的深度学习方法

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-18 DOI: 10.1109/TIFS.2024.3518069

Jing Yan;Yuhan Zheng;Xian Yang;Cailian Chen;Xinping Guan

{"title":"Privacy-Preserving Localization for Underwater Acoustic Sensor Networks: A Differential Privacy-Based Deep Learning Approach","authors":"Jing Yan;Yuhan Zheng;Xian Yang;Cailian Chen;Xinping Guan","doi":"10.1109/TIFS.2024.3518069","DOIUrl":"10.1109/TIFS.2024.3518069","url":null,"abstract":"Localization is a key premise for implementing the applications of underwater acoustic sensor networks (UASNs). However, the inhomogeneous medium and the open feature of underwater environment make it challenging to accomplish the above task. This paper studies the privacy-preserving localization issue of UASNs with consideration of direct and indirect data threats. To handle the direct data threat, a privacy-preserving localization protocol is designed for sensor nodes, where the mutual information is adopted to acquire the optimal noises added on anchor nodes. With the collected range information from anchor nodes, a ray tracing model is employed for sensor nodes to compensate the range bias caused by straight-line propagation. Then, a differential privacy (DP) based deep learning localization estimator is designed to calculate the positions of sensor nodes, and the perturbations are added to the forward propagation of deep learning framework, such that the indirect data leakage can be avoided. Besides that, the theory analyses including the Cramer-Rao Lower Bound (CRLB), the privacy budget and the complexity are provided. Main innovations of this paper include: 1) the mutual information-based localization protocol can acquire the optimal noise over the traditional noise-adding mechanisms; 2) the DP-based deep learning estimator can avoid the leakage of training data caused by overfitting in traditional deep learning-based solutions. Finally, simulation and experimental results are both conducted to verify the effectiveness of our approach.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"737-752"},"PeriodicalIF":6.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849470","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

Robust AI-Synthesized Speech Detection Using Feature Decomposition Learning and Synthesizer Feature Augmentation

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-18 DOI: 10.1109/tifs.2024.3520001

Kuiyuan Zhang, Zhongyun Hua, Yushu Zhang, Yifang Guo, Tao Xiang

引用次数: 0

Query-Efficient Model Inversion Attacks: An Information Flow View

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-18 DOI: 10.1109/tifs.2024.3518779

Yixiao Xu, Binxing Fang, Mohan Li, Xiaolei Liu, Zhihong Tian

引用次数: 0

Multi-Level Resource-Coherented Graph Learning for Website Fingerprinting Attacks 针对网站指纹攻击的多层次资源连贯图学习

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-18 DOI: 10.1109/TIFS.2024.3520014

Bo Gao;Weiwei Liu;Guangjie Liu;Fengyuan Nie;Jianan Huang

{"title":"Multi-Level Resource-Coherented Graph Learning for Website Fingerprinting Attacks","authors":"Bo Gao;Weiwei Liu;Guangjie Liu;Fengyuan Nie;Jianan Huang","doi":"10.1109/TIFS.2024.3520014","DOIUrl":"10.1109/TIFS.2024.3520014","url":null,"abstract":"Deep learning-based website fingerprinting (WF) attacks dominate website traffic classification. In the real world, the main challenges limiting their effectiveness are, on the one hand, the difficulty in countering the effect of content updates on the basis of accurate descriptions of page features in traffic representations. On the other hand, the model’s accuracy relies on training numerous samples, requiring constant manual labeling. The key to solving the problem is to find a website traffic representation that can stably and accurately display page features, as well as to perform self-supervised learning that is not reliant on manual labeling. This study introduces the multi-level resource-coherented graph convolutional neural network (MRCGCN), a self-supervised learning-based WF attack. It analyzes website traffic using resources as the basic unit, which are coarser than packets, ensuring the page’s unique resource layout while improving the robustness of the representations. Then, we utilized an echelon-ordered graph kernel function to extract the graph topology as the label for website traffic. Finally, a two-channel graph convolutional neural network is designed for constructing a self-supervised learning-based traffic classifier. We evaluated the WF attacks using real data in both closed- and open-world scenarios. The results demonstrate that the proposed WF attack has superior and more comprehensive performance compared to state-of-the-art methods.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"693-708"},"PeriodicalIF":6.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849471","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

IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer IFViT：通过视觉转换器进行指纹匹配的可解释固定长度表示法

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-18 DOI: 10.1109/TIFS.2024.3520015

Yuhang Qiu;Honghui Chen;Xingbo Dong;Zheng Lin;Iman Yi Liao;Massimo Tistarelli;Zhe Jin

{"title":"IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer","authors":"Yuhang Qiu;Honghui Chen;Xingbo Dong;Zheng Lin;Iman Yi Liao;Massimo Tistarelli;Zhe Jin","doi":"10.1109/TIFS.2024.3520015","DOIUrl":"10.1109/TIFS.2024.3520015","url":null,"abstract":"Determining dense feature points on fingerprints used in constructing deep fixed-length representations for accurate matching, particularly at the pixel level, is of significant interest. To explore the interpretability of fingerprint matching, we propose a multi-stage interpretable fingerprint matching network, namely Interpretable Fixed-length Representation for Fingerprint Matching via Vision Transformer (IFViT), which consists of two primary modules. The first module, an interpretable dense registration module, establishes a Vision Transformer (ViT)-based Siamese Network to capture long-range dependencies and the global context in fingerprint pairs. It provides interpretable dense pixel-wise correspondences of feature points for fingerprint alignment and enhances the interpretability in the subsequent matching stage. The second module takes into account both local and global representations of the aligned fingerprint pair to achieve an interpretable fixed-length representation extraction and matching. It employs the ViTs trained in the first module with the additional fully connected layer and retrains them to simultaneously produce the discriminative fixed-length representation and interpretable dense pixel-wise correspondences of feature points. Extensive experimental results on diverse publicly available fingerprint databases demonstrate that the proposed framework not only exhibits superior performance on dense registration and matching but also significantly promotes the interpretability in deep fixed-length representations-based fingerprint matching.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"559-573"},"PeriodicalIF":6.3,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849468","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

Stealthiness Assessment of Adversarial Perturbation: From A Visual Perspective 对抗性干扰的隐蔽性评估：从视觉角度

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-18 DOI: 10.1109/tifs.2024.3520016

Hangcheng Liu, Yuan Zhou, Ying Yang, Qingchuan Zhao, Tianwei Zhang, Tao Xiang

引用次数: 0

Learnability of Optical Physical Unclonable Functions Through the Lens of Learning with Errors

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-16 DOI: 10.1109/tifs.2024.3518065

Apollo Albright, Boris Gelfand, Michael Dixon

引用次数: 0

Analyze and Improve Differentially Private Federated Learning: A Model Robustness Perspective 分析和改进差异化私有联合学习：模型鲁棒性视角