IEEE Transactions on Information Forensics and Security最新文献

{"title":"AuthScatter: Accurate, Robust, and Scalable Mutual Authentication in Physical Layer for Backscatter Communications","authors":"Yifan Zhang;Boxuan Xie;Yishan Yang;Zheng Yan;Riku Jäntti;Zhu Han","doi":"10.1109/TIFS.2025.3585453","DOIUrl":"10.1109/TIFS.2025.3585453","url":null,"abstract":"Backscatter communication (BC) enables resource-constrained backscatter devices (BDs) to communicate by reflecting signals from external radio frequency sources (RFSs), thereby avoiding active RF components, making it a cutting-edge technology for the ubiquitous Internet of Things (IoT). However, the open nature of BC makes it vulnerable to passive and active attacks, and existing methods fail to offer robust mutual authentication suitable for mobile BC systems while keeping a low computational overhead. To address this issue, we propose AuthScatter, an accurate, robust, and scalable physical-layer mutual authentication scheme between the RFS and multiple BDs by leveraging channel fading and random numbers as a one-time pad to protect the identity key exchange procedure during the authentication. Specifically, AuthScatter constructs shared identity keys as physical-layer fingerprints for efficient identification and employs a challenge-response authentication mechanism to enable secure key exchange between the RFS and the BD. In the authentication, the one-time pad effectively prevents eavesdropping, spoofing, replay, and counterfeiting attacks, while legitimate devices leverage channel reciprocity and random number knowledge to authenticate efficiently without channel estimation or complex processing. It is tailored for high-mobility scenarios by completing the exchange within the channel coherence time while incorporating a key-update mechanism to ensure sustained security in the long term. Additionally, it includes a re-authentication mechanism to enhance resistance against wireless attacks and a batch authentication framework leveraging time-division duplexing (TDD) to enable scalability in large-scale BC deployments. Comprehensive security analysis demonstrates the resistance of AuthScatter to various threats, including eavesdropping, identity spoofing, replay, and counterfeiting attacks. Extensive simulations further validate its high authentication accuracy across diverse channel conditions, robustness against various attack vectors, and scalability with a large number of BDs, highlighting its superiority over state-of-the-art schemes.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"6937-6952"},"PeriodicalIF":6.3,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11071870","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144566339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Mouse Dynamics Authentication System With a Recurrence Plot Image Representation and a Vision Transformer Framework","authors":"Kaushik Mazumdar;Suresh Sundaram","doi":"10.1109/TIFS.2025.3585435","DOIUrl":"10.1109/TIFS.2025.3585435","url":null,"abstract":"In this paper, we propose a system that verifies the authenticity of users based on the manner in which they operate a computer mouse. To begin with, we introduce a recurrence plot representation for encoding the information available in the mouse dynamics. Two image representation variants are suggested, namely the symmetric and asymmetric recurrence plots. Another noteworthy contribution is a modified vision transformer architecture for this task that incorporates key adjustments such as the removal of class token and positional embeddings. Rather, we facilitate a local pattern classification by considering the use of feature aggregation strategy for decision making. Additionally, we incorporate an efficient attention mechanism within the transformer encoder, that reduces both computational and memory complexity by simplifying the attention process. To further boost model performance, we integrate the Gradient Harmonizing Mechanism with binary cross-entropy loss, which dynamically adjusts the loss function based on gradient magnitudes. The proposed system is evaluated on three publicly available datasets, and the results obtained are at par to state-of-the-art methods. To the best of our knowledge, the present proposal is the first of its kind to introduce the utility of recurrence plots in a modified transformer framework.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"6895-6909"},"PeriodicalIF":6.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547149","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":"Dissecting Blockchain Network Partitioning Attacks and Novel Defense for Bitcoin and Ethereum","authors":"Ruonan Chen, Dawei Li, Yang Zhang, Yizhong Liu, Jianwei Liu, Zhenyu Guan, Min Xie, Qianhong Wu, Jianying Zhou, Willy Susilo","doi":"10.1109/tifs.2025.3585468","DOIUrl":"https://doi.org/10.1109/tifs.2025.3585468","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"1 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547150","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":"SCR-Auth: Secure Call Receiver Authentication on Smartphones Using Outer Ear Echoes","authors":"Xiping Sun;Jing Chen;Kun He;Zhixiang He;Ruiying Du;Yebo Feng;Qingchuan Zhao;Cong Wu","doi":"10.1109/TIFS.2025.3584643","DOIUrl":"10.1109/TIFS.2025.3584643","url":null,"abstract":"Receiving calls is one of the most universal functions of smartphones, involving sensitive information and critical operations. Unfortunately, to prioritize convenience, the current call receiving process bypasses smartphone authentication mechanisms (e.g., passwords, fingerprint recognition, and face recognition), leaving a significant security gap. To address this issue, we propose SCR-Auth, a secure call receiver authentication scheme for smartphones that leverages outer ear echoes. It sends inaudible acoustic signals through the earpiece speaker to actively sense the call receiver’s outer ear structure and records the resulting echoes using the top microphone. These echoes are then analyzed to extract unique outer ear biometric information for authentication. It operates implicitly, without requiring extra hardware or imposing additional burden. Comprehensive experiments conducted under diverse conditions demonstrate SCR-Auth’s effectiveness and security, showing an average balanced accuracy of 96.95% and resilience against potential attacks.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"6763-6777"},"PeriodicalIF":6.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533099","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":"SSDALog: Semi-Supervised Domain Adaptation for Incremental Log-Based Anomaly Detection","authors":"Jiyu Tian;Mingchu Li;Liming Chen;Zumin Wang;Xiaoyu Nie;Jing Qin","doi":"10.1109/TIFS.2025.3583483","DOIUrl":"10.1109/TIFS.2025.3583483","url":null,"abstract":"Log-based anomaly detection (LAD) is one of the dominant approaches to improving the reliability and security of software systems. Presently, despite the efficacy demonstrated by state-of-the-art LAD approaches in processing static log events, their performance significantly degrades when confronting changes of log event types from system updates. To construct a reliable LAD model that could adapt well to the evolution of log data, we propose a method grounded in semi-supervised domain adaptation on the rationale of incremental log anomaly detection dubbed as SSDALog, which dynamically updates the model utilizing limited labeled samples to reconcile distributional shifts between evolving and historical data. Specifically, the proposed approach addresses the issue through two primary mechanisms: (i) creation of a cross-domain mixup algorithm, which computes the feature salience of log discrete sequences through occlusion strategy, thus enhancing the adaptability of the model to unknown patterns by mixing evolving features; and (ii) design of an incremental semi-supervised domain adaptation training framework based on noisy label learning to obtain a robust feature extractor, thus improving the generalization ability of the detection model. We empirically assess the efficacy of the SSDALog approach across two publicly available datasets. The experimental results show that our method outperforms the SOTA LAD approach, particularly for evolving systems.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"6607-6619"},"PeriodicalIF":6.3,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533101","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":"Jailbreak Vision Language Models via Bi-Modal Adversarial Prompt","authors":"Zonghao Ying;Aishan Liu;Tianyuan Zhang;Zhengmin Yu;Siyuan Liang;Xianglong Liu;Dacheng Tao","doi":"10.1109/TIFS.2025.3583249","DOIUrl":"10.1109/TIFS.2025.3583249","url":null,"abstract":"In the realm of large vision language models (LVLMs), jailbreak attacks serve as a red-teaming approach to bypass guardrails and uncover safety implications. Existing jailbreaks predominantly focus on the visual modality, perturbing solely visual inputs in the prompt for attacks. However, they fall short when confronted with aligned models that fuse visual and textual features simultaneously for generation. To address this limitation, this paper introduces the Bi-Modal Adversarial Prompt Attack (BAP), which executes jailbreaks by optimizing textual and visual prompts cohesively. Initially, we adversarially embed universally adversarial perturbations in an image, guided by a few-shot query-agnostic corpus (e.g., affirmative prefixes and negative inhibitions). This process ensures that the adversarial image prompt LVLMs to respond positively to harmful queries. Subsequently, leveraging the image, we optimize textual prompts with specific harmful intent. In particular, we utilize a large language model to analyze jailbreak failures and employ chain-of-thought reasoning to refine textual prompts through a feedback-iteration manner. To validate the efficacy of our approach, we conducted extensive evaluations on various datasets and LVLMs, demonstrating that our BAP significantly outperforms other methods by large margins (+29.03% in attack success rate on average). Additionally, we showcase the potential of our attacks on black-box commercial LVLMs, such as GPT-4o and Gemini. Our code is available at <uri>https://anonymous.4open.science/r/BAP-Jailbreak-Vision-Language-Models-via-Bi-Modal-Adversarial-Prompt-5496</uri>","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"7153-7165"},"PeriodicalIF":6.3,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520709","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":"Generalizable Person Re-Identification From a 3D Perspective: Addressing Unpredictable Viewpoint Changes","authors":"Bingliang Jiao;Lingqiao Liu;Liying Gao;Dapeng Oliver Wu;Guosheng Lin;Peng Wang;Yanning Zhang","doi":"10.1109/TIFS.2025.3583900","DOIUrl":"10.1109/TIFS.2025.3583900","url":null,"abstract":"Most existing Domain Generalizable Person Re-identification (DG-ReID) methods focus on addressing style disparities between domains but often overlook the impact of unpredictable camera view changes, which we have identified as a significant factor responsible for poor generalization performance. To address this issue, we propose a novel approach from a 3D perspective, utilizing a customized 2D-to-3D reconstruction model to convert images captured from arbitrary camera views into canonical view images. However, merely applying a 3D reconstruction model in isolation may not result in improved DG-ReID performance, as reconstruction quality can be influenced by multiple factors, such as insufficient image resolution, extreme viewpoint, and environmental variations. These factors may lead to error accumulation and the loss of critical discriminative clues in the reconstructed results. To address this difficulty, we propose fusing the canonical view image with the original image using a transformer-based module. The transformer’s cross-attention mechanism is ideal for aligning and fusing the key semantic clues of the original image with the canonical view image, compensating for reconstruction errors. We demonstrate the effectiveness of our method through extensive experiments in various evaluation settings, achieving superior DG-ReID performance compared to existing approaches. Our approach addresses the impact of unpredictable camera view changes and provides a new perspective for designing DG-ReID methods.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"6576-6591"},"PeriodicalIF":6.3,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520678","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":"Accelerating Private Large Transformers Inference Through Fine-Grained Collaborative Computation","authors":"Yuntian Chen;Zhanyong Tang;Tianpei Lu;Bingsheng Zhang;Zhiying Shi;Zheng Wang","doi":"10.1109/TIFS.2025.3584639","DOIUrl":"10.1109/TIFS.2025.3584639","url":null,"abstract":"Homomorphic encryption (HE) and secret sharing (SS) enable computations on encrypted data, providing significant privacy benefits for large transformer-based models (TBM) in sensitive sectors like medicine and finance. However, private TBM inference incurs significant costs due to the coarse-grained application of HE and SS. We present <sc>FASTLMPI</small>, a new approach to accelerate private TBM inference through fine-grained computation optimization. Specifically, through the fine-grained co-design of homomorphic encryption and secret sharing, <sc>FASTLMPI</small> achieves efficient protocols for matrix multiplication, SoftMax, LayerNorm, and GeLU. In addition, <sc>FASTLMPI</small> introduces a precise segmented approximation technique for differentiable non-linear functions, improving its fitting accuracy while maintaining a low polynomial degree. Compared to solution BOLT (S&P’24), <sc>FASTLMPI</small> shows a remarkable 25.1% to 55.3% decrease in runtime and an impressive 39.0% reduction in communication costs.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"7482-7497"},"PeriodicalIF":6.3,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520636","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":"BDTM: Bidirectional Detection and Traceability Mitigation of LDoS Attacks in SDN","authors":"Xiaopu Ma;Xiancong Li;Yingyan He;Qinglei Qi;He Li","doi":"10.1109/TIFS.2025.3584638","DOIUrl":"10.1109/TIFS.2025.3584638","url":null,"abstract":"Although Software-Defined Networking (SDN) introduces architectural innovations, it retains fundamental network properties. As a result, Low-rate Denial of Service (LDoS) attacks, which exploit bottleneck links and TCP congestion control mechanisms, still pose a serious threat to SDN. Currently, to accurately detect LDoS attacks at lower average attack rates, many methods focus on extracting and analyzing single-dimensional features. However, these methods are often complex and offer only limited improvements in detection accuracy. Moreover, critical security vulnerabilities in mainstream mitigation strategies highlight their inability to ensure long-term stability. To this end, we propose BDTM, a cross-dimensional bidirectional detection and traceability mitigation scheme. Through attack parameter estimation with a precision of 0.1s, BDTM achieves precise detection of LDoS attacks that incorporate IP spoofing. In terms of mitigation, we have identified, verified, and resolved critical vulnerabilities in existing mainstream mitigation strategies for the first time. Upon detecting an attack, BDTM rapidly mitigates the ongoing anomaly while performing reverse-flow tracing to pinpoint the attacking host. Ultimately, BDTM enforces port-level isolation targeting the attacker rather than the attack flows, ensuring more effective and comprehensive mitigation. Experimental results demonstrate that BDTM achieves a high detection accuracy of 98.85%, with an average response time of just 5.67s when performing attack traceability.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"6826-6839"},"PeriodicalIF":6.3,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520679","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":"Efficient and Secure Multi-Qubit Broadcast-Based Quantum Federated Learning","authors":"Rui Zhang;Jian Wang;Nan Jiang;Md Armanuzzaman;Ziming Zhao","doi":"10.1109/TIFS.2025.3583901","DOIUrl":"10.1109/TIFS.2025.3583901","url":null,"abstract":"Quantum Federated Learning (QFL) has emerged as a promising research direction by combining the strengths of quantum computing and federated learning. However, existing QFL solutions have consistently failed to simultaneously improve client training efficiency and ensure communication security. In this paper, we present a novel Multi-qubit Broadcast-based QFL framework (<sc>MB-QFL</small>) to address the efficiency and security challenges of existing approaches. The framework employs a novel multi-qubit broadcast protocol and a quantum average method to secure the information transmission process. The multi-qubit broadcast protocol overcomes the limitations of existing protocols by allowing the transmission of an arbitrary S-qubit state from one sender to multiple (Q) receivers, whereas earlier protocols were restricted to broadcast one or two qubit state to recipients. Additionally, we propose an averaging method for quantum states, which exploits the probabilistic cloning technique to achieve aggregation in <sc>MB-QFL</small>. The security analysis demonstrates that <sc>MB-QFL</small> can effectively protect against inference attacks from malicious clients, as well as eavesdropping and intercept-and-resend attacks during communication. The algorithm complexity of <sc>MB-QFL</small> is significantly lower than existing QFLs. Besides, the experimental results indicate that <sc>MB-QFL</small> achieves higher classification accuracy than other QFLs.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"6778-6793"},"PeriodicalIF":6.3,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503879","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}