IEEE Transactions on Information Forensics and Security最新文献_第8页

A Forensic Framework With Diverse Data Generation for Generalizable Forgery Localization 一种具有多种数据生成的法医学框架用于通用伪造定位

IF 8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-09-10 DOI: 10.1109/TIFS.2025.3607251

Yuanhang Huang;Weiqi Luo;Xiaochun Cao;Jiwu Huang

{"title":"A Forensic Framework With Diverse Data Generation for Generalizable Forgery Localization","authors":"Yuanhang Huang;Weiqi Luo;Xiaochun Cao;Jiwu Huang","doi":"10.1109/TIFS.2025.3607251","DOIUrl":"10.1109/TIFS.2025.3607251","url":null,"abstract":"Deep learning-based forensic techniques have emerged as the leading approach for image forgery localization. However, many existing methods struggle with overfitting to the training data, which limits their generalization performance and real-world applicability. To overcome this challenge, we propose a novel forensic framework that incorporates an advanced data augmentation technique. The framework consists of two key components: a generator and a detector. The generator challenges the detector’s learned distribution under constraints of diversity and consistency, ensuring that the generated data diverges from the source domain while maintaining statistical differences related to tampering. The detector, in turn, captures tampering traces from three critical aspects of the tampered image: long-range dependency information, RGB-noise fusion information, and boundary artifacts, resulting in a more comprehensive detection process. By alternating the optimization of the generator and detector, the framework fosters mutual reinforcement, promoting diverse data generation and expanding the distributional coverage, ultimately improving performance. Extensive experiments demonstrate that the proposed method significantly surpasses state-of-the-art approaches in both generalization and robustness, with numerous ablation studies further validating the soundness of the model design.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"9732-9745"},"PeriodicalIF":8.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145035618","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

Toward Open-World Network Intrusion Detection via Open Recognition and Inspection 基于开放识别和检测的开放世界网络入侵检测

IF 8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-09-10 DOI: 10.1109/TIFS.2025.3608666

Lei Du;Yuhan Chai;Yan Jia;Binxing Fang;Hao Li;Zhaoquan Gu

{"title":"Toward Open-World Network Intrusion Detection via Open Recognition and Inspection","authors":"Lei Du;Yuhan Chai;Yan Jia;Binxing Fang;Hao Li;Zhaoquan Gu","doi":"10.1109/TIFS.2025.3608666","DOIUrl":"10.1109/TIFS.2025.3608666","url":null,"abstract":"Deep learning is promising in open-world network intrusion detection, but current deep learning-based methods mainly focus on open recognition with properties that may not always hold and significantly neglect the inspection of unknown samples, increasing open space risks and manual inspection overhead for deployed models. To address these challenges in real-world environments, we propose a novel system, ORI, designed to tackle two critical tasks: 1) open recognition, including classifying known class samples while recognizing unknown ones, and 2) inspection, involving further inspecting samples recognized as unknown. Specifically, we reformulate open recognition as a binary classification task and propose a density-based method to recognize low-density samples as unknown while classifying known class samples with a closed-world classifier, thereby minimizing the risk associated with open spaces. To reduce the inspection overhead of samples recognized as unknown, we treat unknown sample inspection as a constrained clustering task, using a few manually inspected samples as constraints, and then assign labels to the remaining unknown samples via clustering. We evaluate our system against established open recognition and unknown sample inspection baselines through extensive experiments on three public datasets. Additionally, we simulated a security analyst inspecting unknown samples labeled by ORI. The experimental results demonstrate that ORI accurately classifies known class samples, recognizes unknown samples, and effectively labels samples recognized as unknown, enhancing both open recognition and inspection capabilities.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"9832-9847"},"PeriodicalIF":8.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145035958","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

A Semantically Guided and Focused Network for Occluded Person Re-Identification 一个语义引导和聚焦的闭塞人再识别网络

IF 8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-09-10 DOI: 10.1109/TIFS.2025.3608672

Guorong Lin;Shunzhi Yang;Wei-Shi Zheng;Zuoyong Li;Zhenhua Huang

{"title":"A Semantically Guided and Focused Network for Occluded Person Re-Identification","authors":"Guorong Lin;Shunzhi Yang;Wei-Shi Zheng;Zuoyong Li;Zhenhua Huang","doi":"10.1109/TIFS.2025.3608672","DOIUrl":"10.1109/TIFS.2025.3608672","url":null,"abstract":"Person re-identification (ReID) is vital for surveillance, tracking, and criminal investigations, yet occlusions often lead to partial information loss and noisy features that significantly degrade ReID performance. Recent CLIP-based occluded person ReID methods have demonstrated promising performance by leveraging cross-modal alignment, but still face two limitations: first, generic text prompts fail to capture the fine-grained semantics of specific samples; second, there is a lack of effective enhancement mechanisms for hard local features in occlusion scenarios. To overcome these limitations, we propose a Semantically Guided and Focused Network (SGFNet), which comprises three synergistic modules. First, to tackle the absence of fine-grained textual descriptions, we design a Segmentation and Text Generation (STG) module that segments pedestrian regions and generates sample-specific text features, providing detailed text descriptions and spatial information for local pedestrian regions. In addition, in order to accurately extract fine-grained features, we propose a Dual-guided Feature Refinement (DGFR) module. This module leverages a spatial attention mechanism guided by dual-semantic information to enhance discriminative fine-grained features while effectively suppressing interference from irrelevant regions. Finally, building upon the DGFR module, we further propose a Hardness-aware Semantic Focus (HASF) module. This module leverages segmentation cues to assess the difficulty of distinguishing local regions and employs a carefully designed Semantic-driven Focal Triplet loss to specifically enhance hard local feature learning, thereby improving the model’s robustness in feature extraction under occlusion scenarios. Extensive experiments demonstrate the superiority of SGFNet, achieving state-of-the-art performance on three occluded person ReID datasets while maintaining competitive results on three holistic person ReID datasets.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"9716-9731"},"PeriodicalIF":8.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145035957","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

Conjunctive Keyword Search With Dynamic Group-User 基于动态组-用户的合取关键字搜索

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-09-10 DOI: 10.1109/tifs.2025.3607238

Nan Gao, Kai Fan, Zhen Zhao, Willy Susilo, Zhoutong Xiong, Hui Li

引用次数: 0

PCSR: Enabling Cross-Modal Semantic Retrieval With Privacy Preservation PCSR：支持隐私保护的跨模态语义检索

IF 8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-09-09 DOI: 10.1109/TIFS.2025.3607246

Hanqi Zhang;Yandong Zheng;Chang Xu;Liehuang Zhu;Can Zhang

引用次数: 0

An Efficient Adversarial Attack on FCG-Based Android Malware Detection Systems 基于fcg的Android恶意软件检测系统的高效对抗性攻击

IF 8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-09-08 DOI: 10.1109/TIFS.2025.3607270

Heng Li;Bang Wu;Wei Zhou;Wei Yuan;Cuiying Gao;Xinge You;Xiapu Luo

引用次数: 0

Chitin: A Security-Enhanced Proof-of-Stake Protocol With View-Interference Resilience 几丁质：具有视图干扰弹性的安全增强权益证明协议

IF 8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-09-08 DOI: 10.1109/TIFS.2025.3607237

Hanyue Dou;Peifang Ni;Jing Xu

{"title":"Chitin: A Security-Enhanced Proof-of-Stake Protocol With View-Interference Resilience","authors":"Hanyue Dou;Peifang Ni;Jing Xu","doi":"10.1109/TIFS.2025.3607237","DOIUrl":"10.1109/TIFS.2025.3607237","url":null,"abstract":"The Proof-of-Stake (PoS) protocol is emerging as one of the most promising blockchain consensus mechanisms, and Ethereum is also undergoing a significant transition to PoS, specifically by adopting Gasper. However, a particularly critical threat faced by existing view-dependent PoS, such as Gasper, lies in view-interference attacks, exemplified by balance attack and reorg attack. These attacks enable adversaries to prevent honest proposals from being committed, thereby directly compromising the fundamental liveness property of blockchain. Currently, there is no effective solution to mitigate such view-interference attacks. In this paper, we present Chitin, a novel view-dependent PoS protocol that is designed to enhance security and effectively mitigate all varieties of view-interference attacks. The core design of Chitin comprises a common set protocol that leverages an innovative deletion mechanism to achieve both a consistent message set and strong termination, while requiring only minimal support from Trusted Execution Environment through its basic validation module. Furthermore, we prove that Chitin not only satisfies safety and liveness, but also possesses resilience against view-interference attacks. Finally, we implement Chitin and conduct comparisons with existing works. The experimental results show that our protocol exhibits superior efficiency, resulting in significant improvements in throughput ranging from 33%-50%, along with reduced communication costs.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"9568-9583"},"PeriodicalIF":8.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145017540","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

Transferable Stealthy Adversarial Example Generation via Dual-Latent Adaptive Diffusion for Facial Privacy Protection 基于双潜自适应扩散的面部隐私保护可转移隐形对抗示例生成

IF 8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-09-08 DOI: 10.1109/TIFS.2025.3607244

Yuanbo Li;Cong Hu;Xiao-Jun Wu

{"title":"Transferable Stealthy Adversarial Example Generation via Dual-Latent Adaptive Diffusion for Facial Privacy Protection","authors":"Yuanbo Li;Cong Hu;Xiao-Jun Wu","doi":"10.1109/TIFS.2025.3607244","DOIUrl":"10.1109/TIFS.2025.3607244","url":null,"abstract":"The widespread application of deep learning-based face recognition (FR) systems poses significant challenges to the privacy of facial images on social media, as unauthorized FR systems can exploit these images to mine user data. Recent studies have utilized adversarial attack techniques to protect facial privacy against malicious FR systems by generating adversarial examples. However, existing noise-based and makeup-based methods produce adversarial examples with noticeable noise or undesired makeup attributes, and suffers from low transferability issues. In this paper, we propose a novel stealthy-based approach, named Dual-latent Adaptive Diffusion Protection (DADP), which generates transferable stealthy adversarial examples consistent with the source images by the diffusion model to protect facial privacy. DADP effectively harnesses adversarial information within both the semantic and diffusion latent spaces to explore adversarial latent representations. Unlike traditional methods that rely on bounded constraints and sign gradient optimization, DADP employs adaptive optimization to maximize the utilization of adversarial gradient information and introduces latent regularization to constrain the adaptive optimization process, ensuring that the protected faces maintain high privacy and natural appearance. Extensive qualitative and quantitative experiments on the public CelebA-HQ and LADN datasets demonstrate the proposed method crafts more natural-looking stealthy adversarial examples with superior black-box transferability compared to the state-of-the-art methods. The code is released at <uri>https://github.com/LiYuanBoJNU/DADP</uri>","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"9427-9440"},"PeriodicalIF":8.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145017844","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

GraphBGP: BGP Anomaly Detection Based on Dynamic Graph Learning GraphBGP：基于动态图学习的BGP异常检测

IF 8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-09-08 DOI: 10.1109/TIFS.2025.3607239

Zheng Wu;Yanbiao Li;Xin Wang;Zulong Diao;Weibei Fan;Fu Xiao;Gaogang Xie

{"title":"GraphBGP: BGP Anomaly Detection Based on Dynamic Graph Learning","authors":"Zheng Wu;Yanbiao Li;Xin Wang;Zulong Diao;Weibei Fan;Fu Xiao;Gaogang Xie","doi":"10.1109/TIFS.2025.3607239","DOIUrl":"10.1109/TIFS.2025.3607239","url":null,"abstract":"Detecting anomalous BGP (Border Gateway Protocol) messages is critical for securing inter-domain routing systems over autonomous system (AS)-level networks. The dynamic nature of routing policies, massive scale of global routes, and incomplete global topology visibility make BGP anomalies exceptionally challenging to identify—let alone trace back to malicious or misconfigured ASes. To effectively overcome these barriers, this paper proposes GraphBGP, a novel BGP anomaly detection method that dynamically constructs real-time AS-level topologies, achieves precise anomaly detection and classification, and accurately traces malicious or misconfigured ASes. Specifically, to address the evolving nature of BGP routing status, GraphBGP constructs an attributed AS-level graph that dynamically integrates node and edge attributes. It intelligently tracks BGP updates to refresh this graph efficiently. Leveraging this enriched, up-to-date representation, GraphBGP employs tailored detection and tracing models grounded in graph convolutional networks (GCNs), enabling precise anomaly identification and source tracing. Comprehensive experiments with real-world and synthetic datasets demonstrate that GraphBGP achieves state-of-the-art anomaly detection accuracy while significantly reducing inference time, even under partial BGP network visibility. Furthermore, GraphBGP precisely traces malicious or misconfigured ASes within a short time period of 7 milliseconds after anomaly detection, enabling rapid mitigation.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"9864-9877"},"PeriodicalIF":8.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145017208","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

Unsupervised Domain Adaptation Person Re-Identification: Bridged by Feature Fusion Transitional Domain 无监督域自适应人物再识别：特征融合过渡域桥接

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-09-08 DOI: 10.1109/tifs.2025.3607258

Qing Tian, Xiang Liu, Jixin Sun, Jun Wan, Zhen Lei

引用次数: 0