IEEE Transactions on Information Forensics and Security最新文献_第4页

Bi-Stream Coteaching Network for Weakly-Supervised Deepfake Localization in Videos

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-11 DOI: 10.1109/TIFS.2025.3533906

Zhaoyang Li;Zhu Teng;Baopeng Zhang;Jianping Fan

{"title":"Bi-Stream Coteaching Network for Weakly-Supervised Deepfake Localization in Videos","authors":"Zhaoyang Li;Zhu Teng;Baopeng Zhang;Jianping Fan","doi":"10.1109/TIFS.2025.3533906","DOIUrl":"10.1109/TIFS.2025.3533906","url":null,"abstract":"With the rapid evolution of deepfake technologies, attackers can arbitrarily alter the intended message of a video by modifying just a few frames. To this extent, simplistic binary judgments of entire videos increasingly seem less convincing and interpretable. Although numerous efforts have been made to develop fine-grained interpretations, these typically depend on elaborate annotations, which are both costly and challenging to obtain in real-world scenarios. To push the related frontier research, we introduce a novel task called Weakly-Supervised Deepfake Localization (WSDL), which aims to identify manipulated frames only with cushy video-level labels. Meanwhile, we propose a new framework named Bi-stream coteaching Deepfake Localization (CoDL), which advances the WSDL task through a progressive mutual refinement strategy across complementary spatial and temporal modalities. The CoDL framework incorporates an inconsistency perception module that discerns subtle forgeries by assessing spatial and temporal incoherence, and a prototype-based enhancement module that mitigates frame noise and amplifies discrepancies to create a robust feature space. Additionally, a progressive coteaching mechanism is implemented to facilitate the exchange of valuable knowledge between modalities, enhancing the detection of subtle frame-level forgery features and thereby improving the model’s generalization capabilities. Extensive experiments are conducted to demonstrate the superiority of our approach, particularly achieving an impressive 8.83% improvement in AUC on highly compressed datasets when learning from weak supervision.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"1724-1738"},"PeriodicalIF":6.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143393042","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

Advancing Visible-Infrared Person Re-Identification: Synergizing Visual-Textual Reasoning and Cross-Modal Feature Alignment

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-11 DOI: 10.1109/TIFS.2025.3539946

Yuxuan Qiu;Liyang Wang;Wei Song;Jiawei Liu;Zhiping Shi;Na Jiang

{"title":"Advancing Visible-Infrared Person Re-Identification: Synergizing Visual-Textual Reasoning and Cross-Modal Feature Alignment","authors":"Yuxuan Qiu;Liyang Wang;Wei Song;Jiawei Liu;Zhiping Shi;Na Jiang","doi":"10.1109/TIFS.2025.3539946","DOIUrl":"10.1109/TIFS.2025.3539946","url":null,"abstract":"Visible-infrared person re-identification (VI-ReID) is a critical cross-modality fine-grained classification task with significant implications for public safety and security applications. Existing VI-ReID methods primarily focus on extracting modality-invariant features for person retrieval. However, due to the inherent lack of texture information in infrared images, these modality-invariant features tend to emphasize global contexts. Consequently, individuals with similar silhouettes are often misidentified, posing potential risks to security systems and forensic investigations. To address this problem, this paper innovatively introduces natural language descriptions to learn the global-local contexts for VI-ReID. Specifically, we design a framework that jointly optimizes visible-infrared alignment plus (VIAP) and visual-textual reasoning (VTR), and introduces local-global joint measure (LJM) to enhance the metric, while proposing a human-LLM collaborative approach to incorporate textual descriptions into existing cross-modal person re-identification datasets. VIAP achieves cross-modal alignment between RGB and IR. It can explicitly utilize designed frequency-aware modality alignment and relationship-reinforced fusion to explore the potential of local cues in global features and modality-invariant information. VTR proposes pooling selection and dual-level reasoning mechanisms to force the image encoder to pay attention to significant regions based on textual descriptions. LJM proposes introducing local feature distances into the measure stage metric to enhance the relevance of matching using fine-grained information. Extensive experimental results on the popular SYSU-MM01 and RegDB datasets show that the proposed method significantly outperforms state-of-the-art approaches. The dataset is publicly available at <uri>https://github.com/qyx596/vireid-caption</uri>.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2184-2196"},"PeriodicalIF":6.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143393041","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

No Time for Remodulation: A PHY Steganographic Symbiotic Channel Over Constant Envelope

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-10 DOI: 10.1109/TIFS.2025.3540290

Jiahao Liu;Caihui Du;Jihong Yu;Jiangchuan Liu;Huan Qi

{"title":"No Time for Remodulation: A PHY Steganographic Symbiotic Channel Over Constant Envelope","authors":"Jiahao Liu;Caihui Du;Jihong Yu;Jiangchuan Liu;Huan Qi","doi":"10.1109/TIFS.2025.3540290","DOIUrl":"10.1109/TIFS.2025.3540290","url":null,"abstract":"Physical layer steganography plays a key role in physical layer security. Yet most works are strongly modulation-sensitive and have to modify the modulation at the baseband. However, these methods cannot work with wireless devices whose baseband modulations cannot be software-defined. To overcome these drawbacks, we propose an analog solution that uses a symbiotic hardware component designed, called Pluggable Cloak, connecting to the radio frequency front end (RFFE) to establish a steganographic symbiotic channel (SSC) over constant envelope physical layer (CE-PHY) in 2.4GHz ISM band, such as Bluetooth, ZigBee and 802.11b Wi-Fi, to hide information. The advantage lies in enabling secure transmission of the deployed devices that are not software-defined with this pluggable hardware. Specifically, Pluggable Cloak analogously modulates the amplitude of CE-PHY, so that sensitive information can be securely sent to a customized receiver without being detected by regular CE receivers. To further protect hidden information from the detection of a malicious adversary, we propose methods to randomize the SSC. We develop a lightweight prototype to evaluate symbiosis, undetectability, and throughput. The results show that the symbol error rates (SERs) of the sensitive data received and regular CE data are lower than <inline-formula> <tex-math>$10^{-5}$ </tex-math></inline-formula> at the customized receiver. In contrast, the SER of the sensitive data is close to 1 in the adversary, confirming the effectiveness of the SSC technique.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2197-2211"},"PeriodicalIF":6.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143385650","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

Dual Consistency Regularization for Generalized Face Anti-Spoofing

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-10 DOI: 10.1109/TIFS.2025.3540659

Yongluo Liu;Zun Li;Lifang Wu

{"title":"Dual Consistency Regularization for Generalized Face Anti-Spoofing","authors":"Yongluo Liu;Zun Li;Lifang Wu","doi":"10.1109/TIFS.2025.3540659","DOIUrl":"10.1109/TIFS.2025.3540659","url":null,"abstract":"Recent Face Anti-Spoofing (FAS) methods have improved generalization to unseen domains by leveraging domain generalization techniques. However, they overlooked the semantic relationships between local features, resulting in suboptimal feature alignment and limited performance. To this end, pixel-wise supervision has been introduced to offer contextual guidance for better feature alignment. Unfortunately, the semantic ambiguity in coarsely designed pixel-wise supervision often leads to misalignment. This paper proposes a novel Dual Consistency Regularization Network (DCRN). It promotes the fine-grained alignment of local features with dense semantic correspondence for FAS. Specifically, a Dual Consistency Learning module (DCL) is devised to capture the inter- and intra-similarity between each region of sample pairs. In this module, a dual consistency regularization learning objective enhances the semantic consistency of local features by minimizing both the variance of inter-similarity and the distance between inter- and intra-similarity. Further, a weight matrix is estimated based on the inter-similarity, representing the possibility that each region belongs to the living class. Based on this weight matrix, WMSE loss is designed to guide the model in avoiding mapping the live regions to the spoofing class, thus alleviating semantic ambiguity in pixel-wise supervision. Extensive experiments on four widely used datasets clearly demonstrate the superiority and high generalization of the proposed DCRN.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2171-2183"},"PeriodicalIF":6.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143385781","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

Lightweight and Dynamic Privacy-Preserving Federated Learning via Functional Encryption

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-10 DOI: 10.1109/tifs.2025.3540312

Boan Yu, Jun Zhao, Kai Zhang, Junqing Gong, Haifeng Qian

引用次数: 0

FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-10 DOI: 10.1109/tifs.2025.3539964

Marvin Xhemrishi, Johan Östman, Antonia Wachter-Zeh, Alexandre Graell i Amat

引用次数: 0

Herd Accountability of Privacy-Preserving Algorithms: A Stackelberg Game Approach

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-10 DOI: 10.1109/TIFS.2025.3540357

Ya-Ting Yang;Tao Zhang;Quanyan Zhu

{"title":"Herd Accountability of Privacy-Preserving Algorithms: A Stackelberg Game Approach","authors":"Ya-Ting Yang;Tao Zhang;Quanyan Zhu","doi":"10.1109/TIFS.2025.3540357","DOIUrl":"10.1109/TIFS.2025.3540357","url":null,"abstract":"AI-driven algorithmic systems are increasingly adopted across various sectors, yet the lack of transparency can raise accountability concerns about claimed privacy protection measures. While machine-based audits offer one avenue for addressing these issues, they are often costly and time-consuming. Herd audit, on the other hand, offers a promising alternative by leveraging collective intelligence from end-users. However, the presence of epistemic disparity among auditors, resulting in varying levels of domain expertise and access to relevant knowledge, captured by the rational inattention model, may impact audit assurance. An effective herd audit must establish a credible accountability threat for algorithm developers, incentivizing them not to breach user trust. In this work, our objective is to develop a systematic framework that explores the impact of herd audits on algorithm developers through the lens of the Stackelberg game. Our analysis reveals the importance of easy access to information and the appropriate design of rewards, as they increase the auditors’ assurance in the audit process. In this context, herd audit serves as a deterrent to negligent behavior. Therefore, by enhancing herd accountability, herd audit contributes to responsible algorithm development, fostering trust between users and algorithms.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2237-2251"},"PeriodicalIF":6.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143385648","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

PAEWS: Public-Key Authenticated Encryption With Wildcard Search Over Outsourced Encrypted Data

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-10 DOI: 10.1109/TIFS.2025.3540606

Fucai Luo;Xingfu Yan;Haining Yang;Xiaofan Zheng

{"title":"PAEWS: Public-Key Authenticated Encryption With Wildcard Search Over Outsourced Encrypted Data","authors":"Fucai Luo;Xingfu Yan;Haining Yang;Xiaofan Zheng","doi":"10.1109/TIFS.2025.3540606","DOIUrl":"10.1109/TIFS.2025.3540606","url":null,"abstract":"Public-key Encryption with Keyword Search (PEKS) is a promising cryptographic mechanism that enables a semi-trusted cloud server to perform (on-demand) keyword searches over encrypted data for data users. Existing PEKS schemes are limited to precise or fuzzy keyword searches, creating a gap given the widespread use of wildcards for rapid searches in real-world applications. To address this issue, several wildcard keyword search schemes have been proposed to support wildcard searches in the public-key setting. However, these schemes suffer from inefficiency and/or inflexibility. Worse yet, they are all vulnerable to (insider) keyword guessing attacks (KGA), which is highly effective when the keyword space is polynomial in size. To address these vulnerabilities, this paper first proposes a new wildcard keyword search scheme called Public-key Encryption with Wildcard Search (PEWS), which is built based on the standard Decisional Diffie-Hellman (DDH) assumption. The complexity of all algorithms in PEWS increases linearly with the keyword length, while remaining almost constant or even decreasing linearly with the number of wildcards. To resist against (insider) KGA, we further extend PEWS into the first Public-key Authenticated Encryption with Wildcard Search (PAEWS) scheme. Our PEWS and PAEWS schemes are highly flexible, supporting searches for any number of wildcards positioned anywhere within the keyword. We conduct a comprehensive performance evaluation of our PEWS and PAEWS, while also comparing PEWS with the state-of-the-art scheme in the public-key setting. The experimental results demonstrate that both PEWS and PAEWS are efficient and practical, and the experimental comparisons illustrate that PEWS achieves approximately <inline-formula> <tex-math>$2 times $ </tex-math></inline-formula> faster computation and reduces communication by at least 50%.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2212-2223"},"PeriodicalIF":6.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143385651","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

On the Impact of Warden Collusion on Covert Communication in Wireless Networks

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-10 DOI: 10.1109/TIFS.2025.3540575

Shuangrui Zhao;Jia Liu;Yulong Shen;Xiaohong Jiang;Tarik Taleb;Norio Shiratori

{"title":"On the Impact of Warden Collusion on Covert Communication in Wireless Networks","authors":"Shuangrui Zhao;Jia Liu;Yulong Shen;Xiaohong Jiang;Tarik Taleb;Norio Shiratori","doi":"10.1109/TIFS.2025.3540575","DOIUrl":"10.1109/TIFS.2025.3540575","url":null,"abstract":"Warden collusion represents a hazardous threat to wireless covert communication, where wardens can combine their observations to perform a more aggressive detection attack. This paper investigates the impact of warden collusion on covert communication in a multi-antenna wireless network consisting of one source, one destination, multiple wardens and interferers. By employing the techniques of Laplace Transform and Cauchy Integral Theorem, we first establish a framework to model the aggregate interference distribution (AID) for covert communication in the network under the typical additive white Gaussian noise (AWGN) and Rayleigh fading channels. Based on the AID results, we then develop theoretical models to reveal the inherent relationship between the collusion intensity and fundamental communication metrics in terms of the covert outage probability, connection outage probability and covert throughput. With the help of these models, we further explore the covert throughput optimization problems and present extensive numerical results to illustrate the impact of warden collusion on the covert throughput under both channel models.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2297-2312"},"PeriodicalIF":6.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143385646","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

Graph Learning on Instruction Stream-augmented CFG for Malware Variant Detection 用于恶意软件变种检测的指令流增强 CFG 图学习

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-07 DOI: 10.1109/tifs.2025.3539937

Jiaxin Mi, Qi Li, Zewei Han, Weilue Liao, Junsong Fu

引用次数: 0