Pub Date : 2025-12-31DOI: 10.1109/tifs.2025.3650025
Zixuan Ding, Ding Wang
{"title":"Hybrid Password Hardening Encryption","authors":"Zixuan Ding, Ding Wang","doi":"10.1109/tifs.2025.3650025","DOIUrl":"https://doi.org/10.1109/tifs.2025.3650025","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"94 1","pages":"1-1"},"PeriodicalIF":6.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893724","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}
In the Segment Routing over IPv6 (SRv6) network, a wide range of network events (e.g., attacks, intrusions, violations, malicious route announcements) may occur. Network management requires real-time monitoring of untrusted and unreliable environments (e.g., unsafe components and devices). Early localization of abnormal links causing violations in the SRv6 network helps minimize the compensation required for service unavailability. However, the overhead of the state-of-the-art methods does not scale efficiently to large-scale SRv6 networks and exhibit poor robustness to addressing various disturbances from unreliable networks. To cope with these challenges, we propose $textsf {Glint}$ , an in-band network telemetry framework to localize abnormal links in SRv6 networks. The key idea of $textsf {Glint}$ is sampling part of the information while the overall information is known. $textsf {Glint}$ provides probabilistic in-band collection to gather segment-level telemetry data, reducing overhead and improving efficiency. $textsf {Glint}$ also proposes distributed verification-based detection to enhance the trustworthiness of security assessments, further improving robustness against disturbances. In addition, we design selective telemetry that reduces telemetry reports while preserving security-relevant visibility. Our evaluations demonstrate that, compared to the state-of-the-art frameworks, $textsf {Glint}$ significantly reduces header bandwidth overhead by 75.6% and memory overhead by 48.7% while reducing false positives. We also implement $textsf {Glint}$ on the Intel Tofino switch, achieving over a 50% reduction in hardware resource consumption compared to existing methods.
{"title":"Glint: Localization of Gray Violations in Untrusted and Unreliable SRv6 Networks","authors":"Kaiyang Zhao;Han Zhang;Yahui Li;Xingang Shi;Zhiliang Wang;Xia Yin;Jiankun Hu;Jianping Wu","doi":"10.1109/TIFS.2025.3649962","DOIUrl":"10.1109/TIFS.2025.3649962","url":null,"abstract":"In the Segment Routing over IPv6 (SRv6) network, a wide range of network events (e.g., attacks, intrusions, violations, malicious route announcements) may occur. Network management requires real-time monitoring of untrusted and unreliable environments (e.g., unsafe components and devices). Early localization of abnormal links causing violations in the SRv6 network helps minimize the compensation required for service unavailability. However, the overhead of the state-of-the-art methods does not scale efficiently to large-scale SRv6 networks and exhibit poor robustness to addressing various disturbances from unreliable networks. To cope with these challenges, we propose <inline-formula> <tex-math>$textsf {Glint}$ </tex-math></inline-formula>, an in-band network telemetry framework to localize abnormal links in SRv6 networks. The key idea of <inline-formula> <tex-math>$textsf {Glint}$ </tex-math></inline-formula> is sampling part of the information while the overall information is known. <inline-formula> <tex-math>$textsf {Glint}$ </tex-math></inline-formula> provides probabilistic in-band collection to gather segment-level telemetry data, reducing overhead and improving efficiency. <inline-formula> <tex-math>$textsf {Glint}$ </tex-math></inline-formula> also proposes distributed verification-based detection to enhance the trustworthiness of security assessments, further improving robustness against disturbances. In addition, we design selective telemetry that reduces telemetry reports while preserving security-relevant visibility. Our evaluations demonstrate that, compared to the state-of-the-art frameworks, <inline-formula> <tex-math>$textsf {Glint}$ </tex-math></inline-formula> significantly reduces header bandwidth overhead by 75.6% and memory overhead by 48.7% while reducing false positives. We also implement <inline-formula> <tex-math>$textsf {Glint}$ </tex-math></inline-formula> on the Intel Tofino switch, achieving over a 50% reduction in hardware resource consumption compared to existing methods.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"812-826"},"PeriodicalIF":8.0,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893726","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}
With the rapid development of intelligent surveillance technology, the massive amount of multimodal data (e.g., videos, images, and text) has imposed higher demands on efficient information retrieval and security. Traditional single-modal retrieval methods struggle to meet practical requirements, making multimodal image-text retrieval a research hotspot in this field. Existing approaches, however, still face challenges in fine-grained semantic alignment and suffer from rigid matching mechanisms. To address these issues, this paper introduces SeaNcr, a novel framework that integrates cross-modal semantic entity alignment with non-correspondence reasoning. Our method constructs class-level entity representations enhanced by saliency-guided masking to capture discriminative semantic features. A pseudo-frozen asynchronous optimization strategy is introduced to maintain semantic consistency across modalities by associating stable entity representations with dynamically updated encoder features. Moreover, to overcome rigid matching, we design a non-correspondence reasoning module that jointly leverages intra-modal similarity and cross-modal mutual nearest neighbor constraints, optimizing matching flexibility and generalization. Extensive experiments validate that SeaNcr significantly enhances cross-modal feature representation and retrieval robustness, achieving state-of-the-art performance on multiple person re-identification benchmarks.
{"title":"Semantic Entity Alignment and Non-Corresponding Reasoning for Text-to-Image Person Re-Identification","authors":"Wanru Peng;Houjin Chen;Yanfeng Li;Jia Sun;Luyifu Chen","doi":"10.1109/TIFS.2025.3649361","DOIUrl":"https://doi.org/10.1109/TIFS.2025.3649361","url":null,"abstract":"With the rapid development of intelligent surveillance technology, the massive amount of multimodal data (e.g., videos, images, and text) has imposed higher demands on efficient information retrieval and security. Traditional single-modal retrieval methods struggle to meet practical requirements, making multimodal image-text retrieval a research hotspot in this field. Existing approaches, however, still face challenges in fine-grained semantic alignment and suffer from rigid matching mechanisms. To address these issues, this paper introduces SeaNcr, a novel framework that integrates cross-modal semantic entity alignment with non-correspondence reasoning. Our method constructs class-level entity representations enhanced by saliency-guided masking to capture discriminative semantic features. A pseudo-frozen asynchronous optimization strategy is introduced to maintain semantic consistency across modalities by associating stable entity representations with dynamically updated encoder features. Moreover, to overcome rigid matching, we design a non-correspondence reasoning module that jointly leverages intra-modal similarity and cross-modal mutual nearest neighbor constraints, optimizing matching flexibility and generalization. Extensive experiments validate that SeaNcr significantly enhances cross-modal feature representation and retrieval robustness, achieving state-of-the-art performance on multiple person re-identification benchmarks.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"772-783"},"PeriodicalIF":8.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929599","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 : 2025-12-26DOI: 10.1109/tifs.2025.3648871
Hegui Zhu, Wenqi Cui, Yue Yan, Ning Han
{"title":"Reinforcing Adversarial Transferability via Negative Class Guided Example Generation","authors":"Hegui Zhu, Wenqi Cui, Yue Yan, Ning Han","doi":"10.1109/tifs.2025.3648871","DOIUrl":"https://doi.org/10.1109/tifs.2025.3648871","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"70 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145844727","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":"Network-Layer Differential Fuzzing for Ethereum","authors":"Fudong Wu, Qianhong Wu, Jia-Ju Bai, Bo Qin, Zhenyu Guan, Willy Susilo","doi":"10.1109/tifs.2025.3648565","DOIUrl":"https://doi.org/10.1109/tifs.2025.3648565","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"1 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145844731","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 : 2025-12-26DOI: 10.1109/tifs.2025.3648867
Marco Rando, Luca Demetrio, Lorenzo Rosasco, Fabio Roli
{"title":"A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection","authors":"Marco Rando, Luca Demetrio, Lorenzo Rosasco, Fabio Roli","doi":"10.1109/tifs.2025.3648867","DOIUrl":"https://doi.org/10.1109/tifs.2025.3648867","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"29 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145844732","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 : 2025-12-26DOI: 10.1109/tifs.2025.3648873
Yaguan Qian, Xucheng Zhu, Qiqi Bao, Fei Yu, Shouling Ji, Zhaoquan Gu, Wei Wang, Bin Wang, Zhen Lei
{"title":"Exploiting Shared Adversarial Features for Dynamic Attacks in Large Vision-Language Models","authors":"Yaguan Qian, Xucheng Zhu, Qiqi Bao, Fei Yu, Shouling Ji, Zhaoquan Gu, Wei Wang, Bin Wang, Zhen Lei","doi":"10.1109/tifs.2025.3648873","DOIUrl":"https://doi.org/10.1109/tifs.2025.3648873","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"4 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145844729","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 : 2025-12-25DOI: 10.1109/tifs.2025.3648540
Ling Li, Cheng Guo, Xinyu Tang, Kim-Kwang Raymond Choo, Yining Liu
{"title":"SSAA: Secure Semi-Asynchronous Aggregation for Decentralized Federated Learning on Heterogeneous Devices","authors":"Ling Li, Cheng Guo, Xinyu Tang, Kim-Kwang Raymond Choo, Yining Liu","doi":"10.1109/tifs.2025.3648540","DOIUrl":"https://doi.org/10.1109/tifs.2025.3648540","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"27 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830165","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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