Pub Date : 2026-01-28DOI: 10.1109/TIFS.2026.3658994
Wenjiao Dong;Xi Yang;Nannan Wang
Event camera-based person re-identification (Re-ID) effectively addresses the challenges faced by traditional Re-ID systems, such as privacy leakage, low-light imaging degradation, and motion blur. However, traditional Convolutional Neural Networks (CNNs) struggle to model long-range spatio-temporal dependencies, while the Transformer architecture encounters fundamental conflicts with second-order computational complexity and the high temporal resolution of event streams. Additionally, sparse data leads to wasted computational resources and diluted effective data. In contrast, the Mamba architecture, with its long-term modeling capability and linear complexity, is better suited for event stream data. Therefore, we innovatively explore the potential of VMamba in event camera-based person Re-ID; however, directly using VMamba does not fully leverage the temporal asynchronicity and spatial sparsity inherent in event data. To address this, we design a novel Sparse VMamba framework to construct a more robust spatio-temporal information extraction mechanism. First, we develop a Spatio-Temporal Information Modeling (STIM) module that simultaneously employs CNNs and Gated Recurrent Units (GRUs) for modeling spatial and temporal information. Then, we enhance the robustness of sparse data feature extraction using two strategies: on one hand, we utilize Anti-Noise Contour Enhancement (ANCE) module to improve motion contour features and mitigate sensor pulse noise; on the other hand, we implement Direction-Aware Sparse Perception (DASP) module to encourage the model to extract robust person descriptors. Results on the Event-ReID-v1 and Event-ReID-v2 datasets validate the effectiveness of our approach.
{"title":"Sparse VMamba: Robust Spatio-Temporal Information Modeling for Event Camera Person Re-Identification","authors":"Wenjiao Dong;Xi Yang;Nannan Wang","doi":"10.1109/TIFS.2026.3658994","DOIUrl":"10.1109/TIFS.2026.3658994","url":null,"abstract":"Event camera-based person re-identification (Re-ID) effectively addresses the challenges faced by traditional Re-ID systems, such as privacy leakage, low-light imaging degradation, and motion blur. However, traditional Convolutional Neural Networks (CNNs) struggle to model long-range spatio-temporal dependencies, while the Transformer architecture encounters fundamental conflicts with second-order computational complexity and the high temporal resolution of event streams. Additionally, sparse data leads to wasted computational resources and diluted effective data. In contrast, the Mamba architecture, with its long-term modeling capability and linear complexity, is better suited for event stream data. Therefore, we innovatively explore the potential of VMamba in event camera-based person Re-ID; however, directly using VMamba does not fully leverage the temporal asynchronicity and spatial sparsity inherent in event data. To address this, we design a novel Sparse VMamba framework to construct a more robust spatio-temporal information extraction mechanism. First, we develop a Spatio-Temporal Information Modeling (STIM) module that simultaneously employs CNNs and Gated Recurrent Units (GRUs) for modeling spatial and temporal information. Then, we enhance the robustness of sparse data feature extraction using two strategies: on one hand, we utilize Anti-Noise Contour Enhancement (ANCE) module to improve motion contour features and mitigate sensor pulse noise; on the other hand, we implement Direction-Aware Sparse Perception (DASP) module to encourage the model to extract robust person descriptors. Results on the Event-ReID-v1 and Event-ReID-v2 datasets validate the effectiveness of our approach.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"1889-1901"},"PeriodicalIF":8.0,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070470","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}
Pub Date : 2026-01-28DOI: 10.1109/tifs.2026.3658989
Guozhen Peng, Yunhong Wang, Zhuguanyu Wu, Shaoxiong Zhang, Yuwei Zhao, Ruiyi Zhan, Annan Li
{"title":"From Gradient Analysis to Norm Control: Rethinking Triplet Loss for Gait Recognition","authors":"Guozhen Peng, Yunhong Wang, Zhuguanyu Wu, Shaoxiong Zhang, Yuwei Zhao, Ruiyi Zhan, Annan Li","doi":"10.1109/tifs.2026.3658989","DOIUrl":"https://doi.org/10.1109/tifs.2026.3658989","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"272 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070467","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}
Pub Date : 2026-01-28DOI: 10.1109/tifs.2026.3659044
Yubo Li, Li Zhang, Kai Li, Haoru Su
{"title":"STELLAR: Similarity-based Satellite Federated Learning for Malicious Traffic Recognition","authors":"Yubo Li, Li Zhang, Kai Li, Haoru Su","doi":"10.1109/tifs.2026.3659044","DOIUrl":"https://doi.org/10.1109/tifs.2026.3659044","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"19 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070465","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}
Pub Date : 2026-01-28DOI: 10.1109/tifs.2026.3659046
Hao Yang, Jing Chen, Junjie Shi, Meng Jia, Ruiying Du, Kun He
{"title":"Realhybrid: A Hybrid Blockchain Consensus with Node-Level Switching","authors":"Hao Yang, Jing Chen, Junjie Shi, Meng Jia, Ruiying Du, Kun He","doi":"10.1109/tifs.2026.3659046","DOIUrl":"https://doi.org/10.1109/tifs.2026.3659046","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"40 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070468","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":"Learning Corruption-Invariant Components and Cross-Modal Correspondence for Unsupervised Visible-Infrared Person Re-Identification","authors":"Long Chen, Rui Sun, Xuebin Wang, Guoxi Huang, Jingjing Wu, Wei Jia","doi":"10.1109/tifs.2026.3658991","DOIUrl":"https://doi.org/10.1109/tifs.2026.3658991","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"117 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070477","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}
Pub Date : 2026-01-28DOI: 10.1109/tifs.2026.3658992
Wanhu Nie, Changsheng Zhu
{"title":"PyraMal: Byte-level Malware Detection and Classification via Pyramid Feature Map","authors":"Wanhu Nie, Changsheng Zhu","doi":"10.1109/tifs.2026.3658992","DOIUrl":"https://doi.org/10.1109/tifs.2026.3658992","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"117 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070484","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}
Pub Date : 2026-01-28DOI: 10.1109/tifs.2026.3658990
Zhiyu Pan, Xiongjun Guan, Yongjie Duan, Jianjiang Feng, Jie Zhou
{"title":"Fixed-Length Dense Fingerprint Representation with Alignment and Robust Enhancement","authors":"Zhiyu Pan, Xiongjun Guan, Yongjie Duan, Jianjiang Feng, Jie Zhou","doi":"10.1109/tifs.2026.3658990","DOIUrl":"https://doi.org/10.1109/tifs.2026.3658990","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"17 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070472","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}
Pub Date : 2026-01-28DOI: 10.1109/TIFS.2026.3659045
Hao Yu;Wenjing Yang;Chuan Ma;Lingyuan Meng;Liang Du;Tao Xiang;Xinwang Liu;Kunlun He
Subgraph Federated Learning (FL) has emerged as a promising paradigm for node classification tasks wherein subgraphs derived from a global graph are distributed across multiple devices to mitigate data leakage risks. Similar to other FL systems, subgraph FL faces significant security challenges, particularly from backdoor attacks, an area that remains extensively underexplored. Existing attacks typically follow a two-phase strategy to implant backdoors. However, in subgraph FL, such attacks often lead to Divergence Amplification, a phenomenon characterized by significant parameter discrepancies between normal and backdoored models, thereby compromising attack stealthiness. To tackle this challenge, we propose BEEF, a Backdoor attack with an End-to-End Framework designed for effectiveness, stealth, and durability. Unlike conventional methods, BEEF incorporates a dedicated trigger generator, which is jointly trained with a backdoored model. To increase its stealthiness, BEEF crafts adversarial perturbations as triggers that provoke misclassification while leaving the model’s parameters entirely untouched. Furthermore, by calibrating a subset of low-salience parameters associated with backdoor activation, BEEF ensures stable performance and sustained effectiveness across FL rounds. Comprehensive evaluations across eight datasets, four models, five state-of-the-art attacks, and six aggregation methods demonstrate BEEF’s effectiveness in deceiving GNNs while maintaining minimal impact on normal data performance. Additionally, we adapt BEEF to federated graph classification tasks, broadening its applicability and practicality.
{"title":"A Wolf in Sheep’s Clothing: Unveiling a Stealthy Backdoor Attack in Subgraph Federated Learning","authors":"Hao Yu;Wenjing Yang;Chuan Ma;Lingyuan Meng;Liang Du;Tao Xiang;Xinwang Liu;Kunlun He","doi":"10.1109/TIFS.2026.3659045","DOIUrl":"10.1109/TIFS.2026.3659045","url":null,"abstract":"Subgraph Federated Learning (FL) has emerged as a promising paradigm for node classification tasks wherein subgraphs derived from a global graph are distributed across multiple devices to mitigate data leakage risks. Similar to other FL systems, subgraph FL faces significant security challenges, particularly from backdoor attacks, an area that remains extensively underexplored. Existing attacks typically follow a two-phase strategy to implant backdoors. However, in subgraph FL, such attacks often lead to Divergence Amplification, a phenomenon characterized by significant parameter discrepancies between normal and backdoored models, thereby compromising attack stealthiness. To tackle this challenge, we propose BEEF, a Backdoor attack with an End-to-End Framework designed for effectiveness, stealth, and durability. Unlike conventional methods, BEEF incorporates a dedicated trigger generator, which is jointly trained with a backdoored model. To increase its stealthiness, BEEF crafts adversarial perturbations as triggers that provoke misclassification while leaving the model’s parameters entirely untouched. Furthermore, by calibrating a subset of low-salience parameters associated with backdoor activation, BEEF ensures stable performance and sustained effectiveness across FL rounds. Comprehensive evaluations across eight datasets, four models, five state-of-the-art attacks, and six aggregation methods demonstrate BEEF’s effectiveness in deceiving GNNs while maintaining minimal impact on normal data performance. Additionally, we adapt BEEF to federated graph classification tasks, broadening its applicability and practicality.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"1842-1857"},"PeriodicalIF":8.0,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070469","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}
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