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

Breaking the Paired Sample Barrier in Person Re-Identification: Leveraging Unpaired Samples for Domain Generalization

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-17 DOI: 10.1109/TIFS.2025.3543040

Huafeng Li;Yaoxin Liu;Yafei Zhang;Jinxing Li;Zhengtao Yu

{"title":"Breaking the Paired Sample Barrier in Person Re-Identification: Leveraging Unpaired Samples for Domain Generalization","authors":"Huafeng Li;Yaoxin Liu;Yafei Zhang;Jinxing Li;Zhengtao Yu","doi":"10.1109/TIFS.2025.3543040","DOIUrl":"10.1109/TIFS.2025.3543040","url":null,"abstract":"Domain generalization (DG) for person re-identification (Re-ID) aims to train models on labeled source domains that generalize well to unseen target domains. However, DG for Re-ID faces a major challenge: existing methods rely solely on labeled paired samples to train DG models and are unable to effectively leverage unpaired samples across cameras. In many cases, cross-camera paired samples are extremely scarce and difficult to annotate. To overcome this limitation, we introduce a novel method specifically tailored for Re-ID. This method leverages cross-camera unpaired samples in model training, thereby reducing the dependence on cross-camera paired samples. We refer to this technique as Unpaired-driven DG (U-DG) person Re-ID. The proposed method leverages a robust image encoder to extract identity-consistent features across various camera views. This capability is further enhanced by integrating a multi-camera person identity classifier, which boosts the encoder’s ability to capture consistent identities, even when viewed from different camera perspectives. To address the scarcity of cross-camera paired samples, we devise a unique model training strategy in our method. Specifically, we use the feature vector from the person identity classifier as a single identity prototype. This prototype serves as a reference for generating identity-related prompts across cameras, effectively compensating for the scarcity of cross-camera paired samples during model training. Additionally, we employ a learnable perturbation prompt to mimic appearance variations exhibited by the same individual across different cameras. Our U-DG offers numerous advantages: it can effectively leverage a large number of unpaired samples for model training, compensating for the scarcity of cross-camera paired samples. Moreover, it does not rely solely on cross-camera paired samples, thereby facilitating the construction of training samples. Experimental results on multiple challenging datasets demonstrate that our approach achieves performance comparable to typical DG person Re-ID, highlighting its feasibility and effectiveness. The source code of our method is available at <uri>https://github.com/lhf12278/DGPS</uri>.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2357-2371"},"PeriodicalIF":6.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443697","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

Recovering Reed-Solomon Codes Privately

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-17 DOI: 10.1109/tifs.2025.3543123

Stanislav Kruglik, Han Mao Kiah, Son Hoang Dau, Eitan Yaakobi

引用次数: 0

Adaptive Generation of Privileged Intermediate Information for Visible-Infrared Person Re-Identification

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-14 DOI: 10.1109/tifs.2025.3541969

Mahdi Alehdaghi, Arthur Josi, Rafael M. O. Cruz, Pourya Shamsolameli, Eric Granger

引用次数: 0

Low-Cost First-Order Secure Boolean Masking in Glitchy Hardware - full version*

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-14 DOI: 10.1109/tifs.2025.3541442

Dilip Kumar S.V., Josep Balasch, Benedikt Gierlichs, Ingrid Verbauwhede

引用次数: 0

PEAFOWL: Private Entity Alignment in Multi-Party Privacy-Preserving Machine Learning

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-14 DOI: 10.1109/tifs.2025.3542244

Ying Gao, Huanghao Deng, Zukun Zhu, Xiaofeng Chen, Yuxin Xie, Pei Duan, Peixuan Chen

引用次数: 0

MUFTI: Multi-Domain Distillation-based Heterogeneous Federated Continuous Learning

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-14 DOI: 10.1109/tifs.2025.3542246

Keke Gai, Zijun Wang, Jing Yu, Liehuang Zhu

引用次数: 0

EnvId: A Metric Learning Approach for Forensic Few-Shot Identification of Unseen Environments

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-13 DOI: 10.1109/TIFS.2025.3541534

Denise Moussa;Germans Hirsch;Christian Riess

{"title":"EnvId: A Metric Learning Approach for Forensic Few-Shot Identification of Unseen Environments","authors":"Denise Moussa;Germans Hirsch;Christian Riess","doi":"10.1109/TIFS.2025.3541534","DOIUrl":"https://doi.org/10.1109/TIFS.2025.3541534","url":null,"abstract":"Audio recordings may provide important evidence in criminal investigations. One such case is the forensic association of a recorded audio to its recording location. For example, a voice message may be the only investigative cue to narrow down the candidate sites for a crime. Up to now, several works provide supervised classification tools for closed-set recording environment identification under relatively clean recording conditions. However, in forensic investigations, the candidate locations are case-specific. Thus, supervised learning techniques are not applicable without retraining a classifier on a sufficient amount of training samples for each case and respective candidate set. In addition, a forensic tool has to deal with audio material from uncontrolled sources with variable properties and quality. In this work, we therefore attempt a major step towards practical forensic application scenarios. We propose a representation learning framework called EnvId, short for environment identification. EnvId avoids case-specific retraining by modeling the task as a few-shot classification problem. We demonstrate that EnvId can handle forensically challenging material. It provides good quality predictions even under unseen signal degradations, out-of-distribution reverberation characteristics or recording position mismatches. Code is available at <uri>https://faui1-gitlab.cs.fau.de/mmsec/few-shot-recording-environment-identification</uri>.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2281-2296"},"PeriodicalIF":6.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512903","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

QUEEN: Query Unlearning Against Model Extraction

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-13 DOI: 10.1109/TIFS.2025.3538266

Huajie Chen;Tianqing Zhu;Lefeng Zhang;Bo Liu;Derui Wang;Wanlei Zhou;Minhui Xue

{"title":"QUEEN: Query Unlearning Against Model Extraction","authors":"Huajie Chen;Tianqing Zhu;Lefeng Zhang;Bo Liu;Derui Wang;Wanlei Zhou;Minhui Xue","doi":"10.1109/TIFS.2025.3538266","DOIUrl":"https://doi.org/10.1109/TIFS.2025.3538266","url":null,"abstract":"Model extraction attacks currently pose a non-negligible threat to the security and privacy of deep learning models. By querying the model with a small dataset and using the query results as the ground-truth labels, an adversary can steal a piracy model with performance comparable to the original model. Two key issues that cause the threat are, on the one hand, accurate and unlimited queries can be obtained by the adversary; on the other hand, the adversary can aggregate the query results to train the model step by step. The existing defenses usually employ model watermarking or fingerprinting to protect the ownership. However, these methods cannot proactively prevent the violation from happening. To mitigate the threat, we propose QUEEN (QUEry unlEarNing) that proactively launches counterattacks on potential model extraction attacks from the very beginning. To limit the potential threat, QUEEN has sensitivity measurement and outputs perturbation that prevents the adversary from training a piracy model with high performance. In sensitivity measurement, QUEEN measures the single query sensitivity by its distance from the center of its cluster in the feature space. To reduce the learning accuracy of attacks, for the highly sensitive query batch, QUEEN applies query unlearning, which is implemented by gradient reverse to perturb the softmax output such that the piracy model will generate reverse gradients to worsen its performance unconsciously. Experiments show that QUEEN outperforms the state-of-the-art defenses against various model extraction attacks with a relatively low cost to the model accuracy. The artifact is publicly available at <uri>https://github.com/MaraPapMann/QUEEN</uri>.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2143-2156"},"PeriodicalIF":6.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480766","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

Submodularity-Based False Data Injection Attack Strategy in DC Microgrids

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-13 DOI: 10.1109/TIFS.2025.3541381

Qi Liu;Chengcheng Zhao;Mengxiang Liu;Ruilong Deng;Peng Cheng

{"title":"Submodularity-Based False Data Injection Attack Strategy in DC Microgrids","authors":"Qi Liu;Chengcheng Zhao;Mengxiang Liu;Ruilong Deng;Peng Cheng","doi":"10.1109/TIFS.2025.3541381","DOIUrl":"https://doi.org/10.1109/TIFS.2025.3541381","url":null,"abstract":"Despite significantly enhancing system flexibility and reliability, the adoption of distributed secondary control in DC microgrids (DCmGs) introduces new vulnerabilities to false data injection (FDI) attacks. As a typical FDI attack, the zero trace stealthy (ZTS) attack has been recently disclosed for DCmGs, which can deteriorate the control objective while keeping stealthy to unknown input observer (UIO)-based detectors. In this work, we investigate the optimal deployment of ZTS attacks, where the adversary with limited resources aims to compromise a set of communication links such that the system state convergence error can be maximized. Specifically, we formulate the optimal ZTS attack deployment problem as a combinatorial optimization problem and unveil its NP-hard characteristic. Then, we discover the submodularity in the state convergence error function, enabling us to transform the original NP-hard problem into a tractable submodular maximization problem. Furthermore, based on the submodular optimization theory, we propose a novel distributed algorithm for the optimal ZTS attack deployment in DCmGs, which effectively balances the attack benefits and computation cost. Finally, comparisons between the centralized and distributed algorithms are illustrated through extensive simulations.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2342-2356"},"PeriodicalIF":6.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512849","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

FeCoGraph: Label-Aware Federated Graph Contrastive Learning for Few-Shot Network Intrusion Detection

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-13 DOI: 10.1109/TIFS.2025.3541890

Qinghua Mao;Xi Lin;Wenchao Xu;Yuxin Qi;Xiu Su;Gaolei Li;Jianhua Li

{"title":"FeCoGraph: Label-Aware Federated Graph Contrastive Learning for Few-Shot Network Intrusion Detection","authors":"Qinghua Mao;Xi Lin;Wenchao Xu;Yuxin Qi;Xiu Su;Gaolei Li;Jianhua Li","doi":"10.1109/TIFS.2025.3541890","DOIUrl":"https://doi.org/10.1109/TIFS.2025.3541890","url":null,"abstract":"With increasing cyber attacks over the Internet, network intrusion detection systems (NIDS) have been an indispensable barrier to protecting network security. Taking advantage of automatically capturing topology connections, recent deep graph learning approaches have achieved remarkable performance in distinguishing different types of malicious flows. However, there remain some critical challenges. 1) previous supervised learning methods rely heavily on abundant and high-quality annotated samples, while label annotation requires abundant time and expert knowledge. 2) Centralized methods require all data to be uploaded to a server for learning behavior patterns, which results in high detection latency and critical privacy leakage. 3) Diverse attack scenarios exhibit highly imbalanced distribution, making it hard to characterize abnormal behaviors. To address these issues, we proposed FeCoGraph, a label-aware federated graph contrastive learning framework for intrusion detection in few-shot scenarios. The line graph is introduced to directly process flow embeddings, which are compatible with diverse GNNs. Furthermore, We formulate a graph contrastive learning task to effectively leverage label information, allowing intra-class embeddings more compact than inter-class embeddings. To improve the scalability of NIDS, we utilize federated learning to cover more attack scenarios while protecting data privacy. Experiment results show that FeCoGraph surpass E-graphSAGE with an average 8.36% accuracy on binary classification and 6.77% accuracy on multiclass classification, demonstrating the efficiency of our approach.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2266-2280"},"PeriodicalIF":6.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512783","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