Pub Date : 2026-01-19DOI: 10.1109/tifs.2026.3655520
Shuai Gong, Chaoran Cui, Chunyun Zhang, Wenna Wang, Xiushan Nie, Lei Zhu
{"title":"Federated Domain Generalization via Prompt Learning and Aggregation","authors":"Shuai Gong, Chaoran Cui, Chunyun Zhang, Wenna Wang, Xiushan Nie, Lei Zhu","doi":"10.1109/tifs.2026.3655520","DOIUrl":"https://doi.org/10.1109/tifs.2026.3655520","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"50 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001315","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 : 2026-01-19DOI: 10.1109/tifs.2026.3655900
Xuechi Chen, Mande Xie, Xiangji Meng, Bochang Yang, Tian Wang, Anfeng Liu, Houbing Herbert Song
{"title":"CPRPS: A Cross-Platform Reputation Privacy Sharing for Speed-up Quality Data Collection in Mobile Crowdsensing","authors":"Xuechi Chen, Mande Xie, Xiangji Meng, Bochang Yang, Tian Wang, Anfeng Liu, Houbing Herbert Song","doi":"10.1109/tifs.2026.3655900","DOIUrl":"https://doi.org/10.1109/tifs.2026.3655900","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"37 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001319","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":"The Chosen-Object Attack: Exploiting the Hungarian Matching Loss in Detection Transformers for Fun and Profit","authors":"Tianyi Wang, Cong Wang, Zhenyu Wen, Ruilong Deng, Yuanchao Shu, Peng Cheng, Jiming Chen","doi":"10.1109/tifs.2026.3654868","DOIUrl":"https://doi.org/10.1109/tifs.2026.3654868","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"35 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993331","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":"Privacy-preserving Joint Distribution Analysis for Set-Valued Data via Local Differential Privacy","authors":"Yilin Wang, Xiong Li, Shuai Shang, Wei Liang, Jinjun Chen, Xiaosong Zhang, Keqin Li","doi":"10.1109/tifs.2026.3654799","DOIUrl":"https://doi.org/10.1109/tifs.2026.3654799","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"742 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993468","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}
Coordinated cyber-physical attacks (CCPAs) pose a critical threat to the secure operation of smart power grids. While existing studies often assume simultaneous or sequence-agnostic attack strategies, this paper proposes a sequence-aware CCPA framework that explicitly models the temporal coupling between cyber manipulation and physical sabotage. We enhance the classical load redistribution attack (LRA) by addressing four key limitations: detectability due to infeasible power flows, violation of power balance, omission of post-attack system response, and insensitivity to attack sequencing. Specifically, we formulate two distinct bilevel attack models, namely Cyber-to-Physical (C$rightarrow $ P) and Physical-to-Cyber (P$rightarrow $ C), and solve them via an exact KKT-based Mixed-Integer Linear Programming (MILP) reformulation and a scalable Benders Decomposition (BD) framework. Experiments on IEEE 14-, 57-, and 118-bus systems demonstrate that C$rightarrow $ P attacks induce significantly more line overloads than P$rightarrow $ C, validating the heightened risk of cyber-initiated cascades. Moreover, our BD approach accurately identifies spatial vulnerability hotspots with high fidelity, even when the physical attack budget is extended from $R_{p} = 1$ to $R_{p} = 2$ , confirming the framework’s scalability and practical relevance. The results provide actionable insights for adaptive grid protection against sophisticated, sequential threats.
{"title":"CCPA via Load Redistribution: Sequential Strategies and Vulnerability Analysis in Power Systems","authors":"Huihui Huang;YunKai Song;Qiang Wei;Ruilong Deng;Yangyang Geng;Haowen Chen","doi":"10.1109/TIFS.2026.3654777","DOIUrl":"10.1109/TIFS.2026.3654777","url":null,"abstract":"Coordinated cyber-physical attacks (CCPAs) pose a critical threat to the secure operation of smart power grids. While existing studies often assume simultaneous or sequence-agnostic attack strategies, this paper proposes a sequence-aware CCPA framework that explicitly models the temporal coupling between cyber manipulation and physical sabotage. We enhance the classical load redistribution attack (LRA) by addressing four key limitations: detectability due to infeasible power flows, violation of power balance, omission of post-attack system response, and insensitivity to attack sequencing. Specifically, we formulate two distinct bilevel attack models, namely Cyber-to-Physical (C<inline-formula> <tex-math>$rightarrow $ </tex-math></inline-formula>P) and Physical-to-Cyber (P<inline-formula> <tex-math>$rightarrow $ </tex-math></inline-formula>C), and solve them via an exact KKT-based Mixed-Integer Linear Programming (MILP) reformulation and a scalable Benders Decomposition (BD) framework. Experiments on IEEE 14-, 57-, and 118-bus systems demonstrate that C<inline-formula> <tex-math>$rightarrow $ </tex-math></inline-formula>P attacks induce significantly more line overloads than P<inline-formula> <tex-math>$rightarrow $ </tex-math></inline-formula>C, validating the heightened risk of cyber-initiated cascades. Moreover, our BD approach accurately identifies spatial vulnerability hotspots with high fidelity, even when the physical attack budget is extended from <inline-formula> <tex-math>$R_{p} = 1$ </tex-math></inline-formula> to <inline-formula> <tex-math>$R_{p} = 2$ </tex-math></inline-formula>, confirming the framework’s scalability and practical relevance. The results provide actionable insights for adaptive grid protection against sophisticated, sequential threats.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2207-2219"},"PeriodicalIF":8.0,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993469","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 : 2026-01-15DOI: 10.1109/tifs.2026.3654865
Xiuwen Liu, Yanjiao Chen, Shanchen Pang
{"title":"Decision Boundary-aware Counterfactual Learning against Model Extraction Attacks on Graph Neural Networks","authors":"Xiuwen Liu, Yanjiao Chen, Shanchen Pang","doi":"10.1109/tifs.2026.3654865","DOIUrl":"https://doi.org/10.1109/tifs.2026.3654865","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"39 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972196","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":"Bridging Lab and Industry: Practical SPA-GPT on Cryptosystems Boosted by LSTM and Simulated Annealing","authors":"Ziyu Wang, Yaoling Ding, An Wang, Congming Wei, Jingqi Zhang, Liehuang Zhu","doi":"10.1109/tifs.2026.3654798","DOIUrl":"https://doi.org/10.1109/tifs.2026.3654798","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"177 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972197","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 : 2026-01-14DOI: 10.1109/tifs.2026.3651978
Shuhan Qi, Qinglin Zhao, Zijie Liu, MengChu Zhou, Meng Shen, Peiyun Zhang, Yi Sun
{"title":"Modeling the Performance-Security Trade-off of Gasper’s Block Proposal Mechanism Under Latency-Driven Attacks","authors":"Shuhan Qi, Qinglin Zhao, Zijie Liu, MengChu Zhou, Meng Shen, Peiyun Zhang, Yi Sun","doi":"10.1109/tifs.2026.3651978","DOIUrl":"https://doi.org/10.1109/tifs.2026.3651978","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"60 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972217","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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