Pub Date : 2026-01-22DOI: 10.1109/tifs.2026.3657099
Yizhong Liu, Boyu Zhao, Mingzhe Zhai, Xun Lin, Chenhao Ying, Zhenyu Guan, Dawei Li, Qianhong Wu, Jianwei Liu, Willy Susilo, Robert H. Deng
{"title":"Multi-Leader Byzantine Fault Tolerance in Blockchain: Performance and Security","authors":"Yizhong Liu, Boyu Zhao, Mingzhe Zhai, Xun Lin, Chenhao Ying, Zhenyu Guan, Dawei Li, Qianhong Wu, Jianwei Liu, Willy Susilo, Robert H. Deng","doi":"10.1109/tifs.2026.3657099","DOIUrl":"https://doi.org/10.1109/tifs.2026.3657099","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"68 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043081","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 : 2026-01-22DOI: 10.1109/tifs.2026.3657030
Rumia Sultana, Rémi A. Chou
{"title":"Secret Sharing Schemes from Correlated Random Variables and Rate-Limited Public Communication","authors":"Rumia Sultana, Rémi A. Chou","doi":"10.1109/tifs.2026.3657030","DOIUrl":"https://doi.org/10.1109/tifs.2026.3657030","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"3 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043079","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}
Private protocol reverse engineering is the main way to solve the problem of unknown traffic which brings huge security risks to the current network environment. The network traffic-based protocol reverse engineering approaches are the basis of traffic security supervision and are also widely used and flexible. These approaches utilize multiple algorithms from different perspectives to extract the protocol specifications from messages, but they fail to recognize the importance of message segmentation and do not adequately evaluate the relation of adjacent bytes, leading to imprecise performance. To address these issues, we propose the SLMSP, a self-supervised learning-based message segmentation approach for private protocol reverse engineering in this paper. SLMSP mines the rich information embedded in the word order and word semantics between adjacent bytes through self-supervised learning, and then makes optimal decisions about where the message should be segmented based on the fusion of those information, combing the horizontal inference and vertical correction. After that, SLMSP extracts protocol formats based on fine-grained message segmentation by introducing the progressive sequence merging algorithm. We conduct comprehensive experiments to demonstrate the effectiveness of SLMSP. The experimental results demonstrate that SLMSP achieves the ideal performance both in message segmentation and format inference, and it also has advantages over previous works.
{"title":"Private Protocol Reverse Engineering via Self-Supervised Learning-Based Message Segmentation","authors":"Junchen Li;Guang Cheng;Huimin Tang;Ying Hu;Qinghua Shang","doi":"10.1109/TIFS.2026.3657097","DOIUrl":"10.1109/TIFS.2026.3657097","url":null,"abstract":"Private protocol reverse engineering is the main way to solve the problem of unknown traffic which brings huge security risks to the current network environment. The network traffic-based protocol reverse engineering approaches are the basis of traffic security supervision and are also widely used and flexible. These approaches utilize multiple algorithms from different perspectives to extract the protocol specifications from messages, but they fail to recognize the importance of message segmentation and do not adequately evaluate the relation of adjacent bytes, leading to imprecise performance. To address these issues, we propose the SLMSP, a self-supervised learning-based message segmentation approach for private protocol reverse engineering in this paper. SLMSP mines the rich information embedded in the word order and word semantics between adjacent bytes through self-supervised learning, and then makes optimal decisions about where the message should be segmented based on the fusion of those information, combing the horizontal inference and vertical correction. After that, SLMSP extracts protocol formats based on fine-grained message segmentation by introducing the progressive sequence merging algorithm. We conduct comprehensive experiments to demonstrate the effectiveness of SLMSP. The experimental results demonstrate that SLMSP achieves the ideal performance both in message segmentation and format inference, and it also has advantages over previous works.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"1926-1940"},"PeriodicalIF":8.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043080","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 : 2026-01-22DOI: 10.1109/tifs.2026.3657093
Hua Deng, Letian Sha, Hui Yin, Zheng Qin, Yuying Liu
{"title":"Match on My Own: Fine-Grained Bilateral Access Control with Self-Constrained Matching for Online Social Networks","authors":"Hua Deng, Letian Sha, Hui Yin, Zheng Qin, Yuying Liu","doi":"10.1109/tifs.2026.3657093","DOIUrl":"https://doi.org/10.1109/tifs.2026.3657093","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"29 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043075","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":"Secure and Customized Data Sharing with Identical Sub-Policy and Bilateral Access Control","authors":"Fuyuan Song, Chuan Zhang, Zhangjie Fu, Meng Li, Zheng Qin, Liehuang Zhu","doi":"10.1109/tifs.2026.3657105","DOIUrl":"https://doi.org/10.1109/tifs.2026.3657105","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"17 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043082","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 : 2026-01-22DOI: 10.1109/TIFS.2026.3657032
Jing Yang;Xusheng Cui;Yuehai Chen;Shaoyi Du;Badong Chen;Yuewen Liu
As face recognition systems become more prevalent and various presentation attacks continue to surface, the significance of face anti-spoofing (FAS) has escalated. In real-world scenarios, we can utilize the existing labeled sample sets, and we can also obtain a wide range of unlabeled face samples, which are the target samples that we need to classify. However, the existing cross-domain FAS methods do not fully utilize the target domain data. That is, they only align the overall distribution of features shared by the source and target domains, but cannot complete the alignment of live and spoof features relevant to classification within the source and target domains, resulting in not so good generalization performance in cross-domain scenarios, especially when the target domain is more complex compared to the source domain. To address this issue, we propose a novel domain adaptation approach called Fine-Grained Domain Alignment for Face Anti-Spoofing with Asymmetric Pseudo-Labels (FGDA-APL). In this approach, we initially employ traditional domain alignment methods to achieve preliminary domain alignment, which can be considered as coarse-grained domain alignment. Subsequently, we introduce the Multi-Graph Convolutional Network (MGCN) module, which is utilized to generate asymmetric feature spaces and facilitate cross-supervised pseudo-labels for asymmetric pseudo-labels utilization. Within the MGCN module, features extracted by the feature extractor are guided to achieve feature aggregation, resulting in multiple distinct feature spaces. We hypothesize that pseudo-labels with high confidence in these asymmetric feature spaces can be regarded as reliable pseudo-labels. By cross-supervising the pseudo-labels generated by both the classifier and the MGCN, we ultimately achieve alignment and classification of real and spoofing features within both the source and target domains. Consequently, we achieve superior classification performance on target domain data. Our proposed method has demonstrated state-of-the-art performance across multiple public datasets through extensive experiments.
{"title":"Fine-Grained Domain Alignment for Face Anti-Spoofing With Asymmetric Pseudo-Labels","authors":"Jing Yang;Xusheng Cui;Yuehai Chen;Shaoyi Du;Badong Chen;Yuewen Liu","doi":"10.1109/TIFS.2026.3657032","DOIUrl":"10.1109/TIFS.2026.3657032","url":null,"abstract":"As face recognition systems become more prevalent and various presentation attacks continue to surface, the significance of face anti-spoofing (FAS) has escalated. In real-world scenarios, we can utilize the existing labeled sample sets, and we can also obtain a wide range of unlabeled face samples, which are the target samples that we need to classify. However, the existing cross-domain FAS methods do not fully utilize the target domain data. That is, they only align the overall distribution of features shared by the source and target domains, but cannot complete the alignment of live and spoof features relevant to classification within the source and target domains, resulting in not so good generalization performance in cross-domain scenarios, especially when the target domain is more complex compared to the source domain. To address this issue, we propose a novel domain adaptation approach called Fine-Grained Domain Alignment for Face Anti-Spoofing with Asymmetric Pseudo-Labels (FGDA-APL). In this approach, we initially employ traditional domain alignment methods to achieve preliminary domain alignment, which can be considered as coarse-grained domain alignment. Subsequently, we introduce the Multi-Graph Convolutional Network (MGCN) module, which is utilized to generate asymmetric feature spaces and facilitate cross-supervised pseudo-labels for asymmetric pseudo-labels utilization. Within the MGCN module, features extracted by the feature extractor are guided to achieve feature aggregation, resulting in multiple distinct feature spaces. We hypothesize that pseudo-labels with high confidence in these asymmetric feature spaces can be regarded as reliable pseudo-labels. By cross-supervising the pseudo-labels generated by both the classifier and the MGCN, we ultimately achieve alignment and classification of real and spoofing features within both the source and target domains. Consequently, we achieve superior classification performance on target domain data. Our proposed method has demonstrated state-of-the-art performance across multiple public datasets through extensive experiments.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"1973-1986"},"PeriodicalIF":8.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043083","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 : 2026-01-22DOI: 10.1109/TIFS.2026.3657090
Ran Mao;Zhou Zhang;Zian Zhao;Zhenyu Guan;Peng Yin;Song Bian
Recently, growing interests are developed in optimizing fully homomorphic encryption (FHE) circuits to enable Boolean function evaluations over ciphertexts. While existing works utilize functional bootstrapping (FBS) to efficiently evaluate logic gates, the evaluation efficiency for large-scale circuits remains limited. Recent advances introduce a fast ciphertext conversion method, making it feasible to evaluate look-up tables (LUTs) over homomorphic multiplexer operation. In this work, we propose a new circuit synthesis framework, SALUS, which automatically generates and evaluates gate-level graphs over homomorphic LUTs given an input Boolean circuit. We apply the binary decision diagram (BDD) reordering method and multi-value refresh techniques to efficiently evaluate complex LUTs. Additionally, we propose a heuristic algorithm to merge LUTs in a given circuit into multi-output LUTs. In the experiments, we examine the efficiency of SALUS using a wide range of benchmark suites, including the EPFL and ISCAS benchmark circuits. We show that SALUS achieves a maximum reduction of up to $26times $ in computational latency compared to state-of-the-art homomorphic circuit synthesis method. Furthermore, we evaluate real-world applications, e.g., image filtering and matrix multiplication, and achieve an average speedup of $8.6times $ (with a maximum speedup of $24times $ ) compared to the FBS-based method.
{"title":"SALUS: Large-Scale Homomorphic Circuit Synthesis via Logic-Aware LUT Optimization","authors":"Ran Mao;Zhou Zhang;Zian Zhao;Zhenyu Guan;Peng Yin;Song Bian","doi":"10.1109/TIFS.2026.3657090","DOIUrl":"10.1109/TIFS.2026.3657090","url":null,"abstract":"Recently, growing interests are developed in optimizing fully homomorphic encryption (FHE) circuits to enable Boolean function evaluations over ciphertexts. While existing works utilize functional bootstrapping (FBS) to efficiently evaluate logic gates, the evaluation efficiency for large-scale circuits remains limited. Recent advances introduce a fast ciphertext conversion method, making it feasible to evaluate look-up tables (LUTs) over homomorphic multiplexer operation. In this work, we propose a new circuit synthesis framework, SALUS, which automatically generates and evaluates gate-level graphs over homomorphic LUTs given an input Boolean circuit. We apply the binary decision diagram (BDD) reordering method and multi-value refresh techniques to efficiently evaluate complex LUTs. Additionally, we propose a heuristic algorithm to merge LUTs in a given circuit into multi-output LUTs. In the experiments, we examine the efficiency of SALUS using a wide range of benchmark suites, including the EPFL and ISCAS benchmark circuits. We show that SALUS achieves a maximum reduction of up to <inline-formula> <tex-math>$26times $ </tex-math></inline-formula> in computational latency compared to state-of-the-art homomorphic circuit synthesis method. Furthermore, we evaluate real-world applications, e.g., image filtering and matrix multiplication, and achieve an average speedup of <inline-formula> <tex-math>$8.6times $ </tex-math></inline-formula> (with a maximum speedup of <inline-formula> <tex-math>$24times $ </tex-math></inline-formula>) compared to the FBS-based method.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2002-2016"},"PeriodicalIF":8.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043077","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":"Adversarial Video Promotion Against Text-to-Video Retrieval","authors":"Qiwei Tian, Chenhao Lin, Zhengyu Zhao, Shuai Liu, Qian Li, Chao Shen","doi":"10.1109/tifs.2026.3657094","DOIUrl":"https://doi.org/10.1109/tifs.2026.3657094","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"57 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043076","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.2026.3655514
Xinglin Lian, Yu Zheng, Yan Liu, Fan Zhou, Chunlei Peng, Xinbo Gao
{"title":"Contextual Masking Distillation for Network Traffic Anomaly Detection","authors":"Xinglin Lian, Yu Zheng, Yan Liu, Fan Zhou, Chunlei Peng, Xinbo Gao","doi":"10.1109/tifs.2026.3655514","DOIUrl":"https://doi.org/10.1109/tifs.2026.3655514","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"181 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001351","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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