IEEE Transactions on Information Forensics and Security最新文献_第3页

Contrastive-Generative-Contrastive: Neutralize Subjectivity in Sketch Re-identification 对比-生成-对比：素描再识别中的中性主体性

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-07-24 DOI: 10.1109/tifs.2025.3592535

Zechao Hu, Zhengwei Yang, Hao Li, Zheng Wang

引用次数: 0

An Asymmetric Siamese Transformer with Global-Local Alignment Attention for Visible-X-ray Cross-Modality Package Re-Identification 具有全局-局部对准注意的非对称Siamese变压器用于可见x射线交叉模态封装再识别

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-07-24 DOI: 10.1109/tifs.2025.3592540

Yonggan Wu, Hongrui Yuan, Zichao Yuan, Lingchao Meng, Yueyi Bai, Hongqiang Wang

引用次数: 0

Detecting Lifecycle-Related Concurrency Bugs in ROS Programs via Coverage-Guided Fuzzing 通过覆盖引导模糊测试检测ROS程序中与生命周期相关的并发错误

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-07-24 DOI: 10.1109/tifs.2025.3592562

Si-Miao Gao, Pengcheng Wang, Jia-Ju Bai, Jia-Wei Yu, Haizhou Wang

引用次数: 0

LiSB: Lightweight Secure Boot and Attestation Scheme for IoT and Edge Devices LiSB：物联网和边缘设备的轻量级安全启动和认证方案

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-07-24 DOI: 10.1109/tifs.2025.3592573

Mohamed Younis, Mohammad Ebrahimabadi, Suhee Sanjana Mehjabin, Emily Pozniak, Tamim Sookoor, Naghmeh Karimi

引用次数: 0

An SNR-Aware Feature Reconstruction Method in Radio Frequency Fingerprint Identification 射频指纹识别中感知信噪比的特征重构方法

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-07-24 DOI: 10.1109/tifs.2025.3592567

Junxian Shi, Linning Peng, Lingnan Xie, Hua Fu, Aiqun Hu

引用次数: 0

HABC: A Mutual and Handover Authentication Scheme for Backscatter Communications with High Robustness HABC：一种具有高鲁棒性的反向散射通信互鉴与切换认证方案

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-07-24 DOI: 10.1109/tifs.2025.3592548

Yishan Yang, Zheng Yan, Niya Luo, Jiajun Li, Mianxiong Dong, Kaoru Ota

引用次数: 0

Within 3DMM Space: Exploring Inherent 3D Artifact for Video Forgery Detection 在3DMM空间内：探索视频伪造检测的固有3D伪影

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-07-24 DOI: 10.1109/tifs.2025.3592557

Chunlei Peng, Tian Xu, Decheng Liu, Nannan Wang, Xinbo Gao

引用次数: 0

PERM: Streamlining Cloud Authorization with Flexible and Scalable Policy Enforcement PERM：通过灵活和可扩展的策略实施简化云授权

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-07-24 DOI: 10.1109/tifs.2025.3592519

Yang Luo, Qingni Shen, Zhonghai Wu

引用次数: 0

Open-Vocabulary Prohibited Item Detection for Real-World X-Ray Security Inspection 真实世界x射线安全检查的开放词汇违禁物品检测

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-07-23 DOI: 10.1109/TIFS.2025.3586492

Shuyang Lin;Tong Jia;Hao Wang;Bowen Ma;Mingyuan Li

{"title":"Open-Vocabulary Prohibited Item Detection for Real-World X-Ray Security Inspection","authors":"Shuyang Lin;Tong Jia;Hao Wang;Bowen Ma;Mingyuan Li","doi":"10.1109/TIFS.2025.3586492","DOIUrl":"10.1109/TIFS.2025.3586492","url":null,"abstract":"Computer-aided prohibited item detection is applied in X-ray security inspection to maintain public safety. However, existing prohibited item detectors are limited to a small set of categories in current X-ray datasets, posing potential risks to public security. Since constructing bigger datasets and annotating hundreds of categories is time-consuming and labor-intensive, scaling detectors to more categories with minimal supervision is of great importance. To this end, in this paper, we adopt an open-vocabulary object detection (OVOD) method to detect arbitrary unlabeled novel categories of prohibited item. OVOD methods typically rely on datasets with caption annotations, which are lacking in the domain of prohibited item detection. To support the research on OVOD in X-ray security inspection scenarios, we contribute PIXray Caption dataset, the first X-ray dataset with image-caption pair annotations, which could benchmark and facilitate researches in the community. Further, we propose a novel Open-Vocabulary Prohibited Item Detection (OVPID) network to leverage textual information from captions. OVPID contains two core modules, i.e., Interference Resistant Module (IRM) and Prediction Module (PM). Specifically, IRM includes two submodules, namely Edge Perception (EP) and Foreground Activation (FA), which are designed to address the dilemma of interference caused by overlapping problem and complex background in X-ray images. PM consists of two branches for classification and localization. In classification branch, PM generates more accurate prompts for X-ray dataset via large multimodal model (LMM). In localization branch, PM aligns the student embeddings with both teacher and caption embeddings. Extensive experiments on PIXray Caption dataset demonstrate that OVPID outperforms other OVOD methods by delivering a higher accuracy on novel categories.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"7469-7481"},"PeriodicalIF":6.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694056","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

Knowledge Driven Signal Transformer for Emitter Recognition 知识驱动的发射极识别信号变压器

IF 8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-07-17 DOI: 10.1109/TIFS.2025.3586503

Shurong Ren;Shuyuan Yang;Mengyao Zhan;Zhuoyue Qi;Zhixi Feng

{"title":"Knowledge Driven Signal Transformer for Emitter Recognition","authors":"Shurong Ren;Shuyuan Yang;Mengyao Zhan;Zhuoyue Qi;Zhixi Feng","doi":"10.1109/TIFS.2025.3586503","DOIUrl":"https://doi.org/10.1109/TIFS.2025.3586503","url":null,"abstract":"Recently, deep neural networks (DNNs) based emitter recognition or identification has received increasing interest. However, most of them are purely data-driven and require a large number of labeled instances. In this paper, a new Knowledge Driven Signal Transformer (KDSiT) is proposed, which introduces the knowledge graph (KG) into a signal Transformer (ST) model for accurate emitter recognition in real-world scenarios. On the one hand, KDSiT use a unified multimodal Transformer structure to explore the latent long-range dependencies in signals, and capture the subtle differences of emitters. On the other hand, KDSiT introduces domain knowledge, such as relationships and attributes between emitters, by constructing an emitter knowledge graph. By combining the powerful feature learning capability of DNNs with the rich semantic information in KG, KDSiT can extract more discriminative features of emitters from multimodal learning, to improve the identification accuracy in degraded environments. Extensive experiments are conducted, and the results prove the superiority of KDSiT over its counterparts, especially in the case of low signal-to-noise ratio (SNR), incomplete signals, and a limited number of labeled instances.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"7724-7735"},"PeriodicalIF":8.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725151","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