IEEE Transactions on Information Forensics and Security最新文献_第10页

Deep Prediction and Efficient 3D Mapping of Color Images for Reversible Data Hiding

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-24 DOI: 10.1109/TIFS.2025.3544956

Runwen Hu;Yuhong Wu;Shijun Xiang;Xiaolong Li;Yao Zhao

{"title":"Deep Prediction and Efficient 3D Mapping of Color Images for Reversible Data Hiding","authors":"Runwen Hu;Yuhong Wu;Shijun Xiang;Xiaolong Li;Yao Zhao","doi":"10.1109/TIFS.2025.3544956","DOIUrl":"10.1109/TIFS.2025.3544956","url":null,"abstract":"In the reversible data hiding (RDH) community, both prediction and mapping strategies are vital for reducing distortion. With high prediction performance, small prediction errors can be generated to reduce the embedding distortion. Besides, the efficient mapping strategy can improve the practicality. In this paper, we propose a new RDH method for color images by using convolution neural networks (CNNs) for prediction and an efficient 3D mapping strategy for embedding. At first, each color image is elaborately divided into three isolated image sets so that the proposed deep prediction network (DPN) can exploit more neighboring pixels in the current channel and the correlation between three channels. Then, an efficient 3D mapping strategy is luminously designed by using the symmetry of the 3D prediction error histogram (PEH). The symmetry of 3D PEH has been analyzed in statistical and experimental ways. Based on the proposed deep prediction network and efficient 3D mapping strategy (DPEM), we construct an efficient RDH method for color images. The performance of the proposed DPN is evaluated by comparing it with several predictors on different image datasets. The embedding performance has been demonstrated by hiding information in color images, e.g., the average PSNR value of the Kodak dataset is 63.63 dB with an embedding capacity of 50,000 bits. Furthermore, the experimental results on the ImageNet and PASCAL VOC2012 datasets have shown the proposed RDH method is superior to several state-of-the-art RDH methods. With the introduction of deep learning, the development of the RDH method for color images can be promoted.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2607-2620"},"PeriodicalIF":6.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143486225","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

Screen-Shooting Robust Watermark Based on Style Transfer and Structural Re-Parameterization

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-21 DOI: 10.1109/TIFS.2025.3542992

Guangyong Gao;Xiaoan Chen;Li Li;Zhihua Xia;Jianwei Fei;Yun-Qing Shi

{"title":"Screen-Shooting Robust Watermark Based on Style Transfer and Structural Re-Parameterization","authors":"Guangyong Gao;Xiaoan Chen;Li Li;Zhihua Xia;Jianwei Fei;Yun-Qing Shi","doi":"10.1109/TIFS.2025.3542992","DOIUrl":"10.1109/TIFS.2025.3542992","url":null,"abstract":"In real-world applications, screen capturing represents a significant scenario where this process can induce substantial distortion to the original image. Previous methods for simulating screen-shooting distortion often involved combining different formulas. We found that these simulation methods still have a significant gap compared to real distortions, making it urgently necessary to develop a realistic and credible comprehensive noise layer to achieve robustness against screen-shooting distortion. This paper presents a watermarking scheme capable of withstanding severe screen-shooting distortion. First, a dataset is constructed to train a screen-shooting distortion simulation network based on style transfer. Subsequently, a comprehensive noise layer is built upon this network to achieve robustness against severe screen-shooting distortion. Additionally, this paper incorporates structural re-parameterization techniques into the traditional U-shaped encoder to improve the quality of encoded images. Extensive experiments demonstrate the proposed scheme’s superior performance in terms of robustness and generalization, especially under severe screen-shooting distortion conditions.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2648-2663"},"PeriodicalIF":6.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470582","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

HSM-Based Architecture to Detect Insider Attacks on Server-Side Data

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-21 DOI: 10.1109/TIFS.2025.3544485

Marc Dib;Samuel Pierre

引用次数: 0

Post-Quantum Rollup: Falcon Signature Aggregation Based on SNARG With Enhanced Gates

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-21 DOI: 10.1109/TIFS.2025.3544490

Tianyu Zhaolu;Zhiguo Wan;Huaqun Wang

{"title":"Post-Quantum Rollup: Falcon Signature Aggregation Based on SNARG With Enhanced Gates","authors":"Tianyu Zhaolu;Zhiguo Wan;Huaqun Wang","doi":"10.1109/TIFS.2025.3544490","DOIUrl":"10.1109/TIFS.2025.3544490","url":null,"abstract":"Blockchain layer 2 solutions aim to address scalability issues in Layer 1 networks by improving transaction efficiency and alleviating congestion. The rollup, a well-known Layer 2 scaling protocol, uses an aggregate signature scheme based on the succinct non-interactive argument of knowledge (SNARG) to package transactions. The further promotion of rollup faces the challenge of balancing computation efficiency and communication costs. In addition, with the continuous development of quantum computing, a transition to post-quantum cryptography is considered crucial for long-term security. Our main contribution is an aggregate Falcon signature scheme for post-quantum rollup based on a novel SNARG scheme. The proposed SNARG is based on the Plonkish circuit with enhanced custom gates, referred to as the ECG circuit, and a post-quantum multilinear polynomial commitment scheme (PolyCom). The former can represent more complex operations while also controlling the witness scale. The latter realizes quantum-resistant security for the proposed SNARG and the aggregate signature. In comparison to the aggregate signature based on Orion, our scheme achieves lower aggregation and communication costs. Performance analysis indicates a 38 % decrease in aggregation time and a 88 % decrease in communication costs. As an additional contribution, we introduce a novel polynomial interactive oracle proof (PolyIOP) protocol for the ECG circuit, which can combine with a multilinear PolyCom scheme to form a SNARG protocol with lower computation and communication overhead compared to existing schemes.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2899-2914"},"PeriodicalIF":6.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470580","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

Hamster: A Fast Synchronous Byzantine Fault Tolerant Protocol

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-20 DOI: 10.1109/TIFS.2025.3544034

Ximing Fu;Mo Li;Qingming Zeng;Tianyang Li;Shenghao Yang;Yonghui Guan;Chuanyi Liu

引用次数: 0

A New Functional Encryption Scheme Supporting Privacy-Preserving Maximum Similarity for Web Service Platforms

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-20 DOI: 10.1109/TIFS.2025.3544072

Zhenhua Chen;Kaili Long;Junrui Xie;Qiqi Lai;Yilei Wang;Ni Li;Luqi Huang;Aijun Ge

{"title":"A New Functional Encryption Scheme Supporting Privacy-Preserving Maximum Similarity for Web Service Platforms","authors":"Zhenhua Chen;Kaili Long;Junrui Xie;Qiqi Lai;Yilei Wang;Ni Li;Luqi Huang;Aijun Ge","doi":"10.1109/TIFS.2025.3544072","DOIUrl":"10.1109/TIFS.2025.3544072","url":null,"abstract":"As a common metric, maximum similarity between two objects is widely employed by web platforms to provide matching services. However, the calculation of maximum similarity involves numerous sensitive or confidential users’ data, and the web platform server is often not trusted who might peep these data out of curiosity, or even worse sell them to unauthorized entities to make profits. Therefore, many research lines on functional encryption have been suggested and studied on how to calculate the maximum similarity while ensure the privacy of users’ data. Unfortunately, all of them will divulge some intermediate results to the web platform server when processing this issue. In this paper we present a new functional encryption scheme supporting privacy-preserving maximum similarity, which enables the web service platforms to figure out the maximum similarity without learning anything else about their data. Moreover, we provide a formal analysis to prove the security of the proposed scheme, followed by some experimental evaluations and comprehensive comparisons with the related works. It shows that, our scheme is the first functional encryption realization on maximum similarity without divulging the intermediate result and meanwhile achieve a higher security-function privacy, as well as a traditional data privacy.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2621-2631"},"PeriodicalIF":6.3,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462624","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

Towards Effective Rotation Generalization in UAV Object Re-Identification

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-20 DOI: 10.1109/TIFS.2025.3544088

Shuoyi Chen;Mang Ye;Yan Huang;Bo Du

{"title":"Towards Effective Rotation Generalization in UAV Object Re-Identification","authors":"Shuoyi Chen;Mang Ye;Yan Huang;Bo Du","doi":"10.1109/TIFS.2025.3544088","DOIUrl":"10.1109/TIFS.2025.3544088","url":null,"abstract":"UAV surveillance offers a unique aerial perspective, enabling the monitoring of large areas and capturing targets from angles that fixed ground cameras cannot achieve. UAV-based object re-identification (ReID) differs from the extensively studied city camera scenarios, as it involves identifying specific objects in aerial images captured from a dynamic bird’s-eye view. The challenge lies in the significant variation in object perspectives and the often uncertain rotational changes captured by UAVs. Existing ReID methods designed for city cameras struggle to adapt to these rotational variations. To address these challenges, we propose a Transformer-based learnable rotation generalization enhancement method specifically for UAV-based ReID. To improve the model’s adaptability to uncertain rotational changes, we introduce a learnable feature-level rotation simulation technique that generates multiple rotated features. Building on this, we design a rotation diversification loss to decorrelate different rotated features, ensuring a rich feature representation. Additionally, to mitigate the negative effects of image-level rotation augmentation, we propose instance-level and distribution-level rotation invariance regularization. This approach establishes explicit associations between images and their rotated counterparts, facilitating the learning of visually consistent rotation-invariant features. Instance-level constraints ensure that detailed features remain consistent during rotation, while distribution-level constraints maintain the model’s semantic understanding. Notably, our method demonstrates strong versatility, covering a wide range of objects, including persons, vehicles, and various animals. Evaluations on multiple UAV-collected person and vehicle ReID datasets, as well as several animal datasets, consistently show outstanding performance, underscoring its robustness and adaptability to the unique challenges posed by UAV-based ReID.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2593-2606"},"PeriodicalIF":6.3,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462594","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

Understanding the Security Risks of Websites Using Cloud Storage for Direct User File Uploads

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-20 DOI: 10.1109/TIFS.2025.3544082

Yuanchao Chen;Yuwei Li;Yuliang Lu;Zulie Pan;Yuan Chen;Shouling Ji;Yu Chen;Yang Li;Yi Shen

{"title":"Understanding the Security Risks of Websites Using Cloud Storage for Direct User File Uploads","authors":"Yuanchao Chen;Yuwei Li;Yuliang Lu;Zulie Pan;Yuan Chen;Shouling Ji;Yu Chen;Yang Li;Yi Shen","doi":"10.1109/TIFS.2025.3544082","DOIUrl":"10.1109/TIFS.2025.3544082","url":null,"abstract":"With the rising demand for website data storage, leveraging cloud storage services for vast user file storage has become prevalent. Nowadays, a new file upload scenario has been introduced, allowing web users to upload files directly to the cloud storage service. This new scenario offers convenience but involves more roles (i.e., web users, web servers, and cloud storage services) and their interactions, bringing new security threats. In this paper, we perform the first systematic security study in this scenario. With in-depth analysis, we identify six new types of vulnerabilities and conduct large-scale real-world measurements on the top 500 Alexa Rank websites. Among these websites, 182 (36.4%) use cloud storage services, illustrating the widespread use of the cloud. Then, we perform a detailed analysis of 28 popular websites that allow user upload. Surprisingly, they all have at least one of the six vulnerabilities. Totally, we discover 79 new vulnerabilities and responsibly report them to the websites. Many popular websites respond positively, including Google, Reddit, and CSDN. We discuss the root causes of these vulnerabilities and propose possible mitigation methods. In summary, our work offers significant value in understanding the security risks of cloud storage services for websites and facilitating future research.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2677-2692"},"PeriodicalIF":6.3,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10896886","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462593","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}

引用次数: 0

Can We Trust the Similarity Measurement in Federated Learning?

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-20 DOI: 10.1109/tifs.2024.3516567

Zhilin Wang, Qin Hu, Xukai Zou, Pengfei Hu, Xiuzhen Cheng

引用次数: 0

RSNet: Region-Specific Network for Contactless Palm Vein Authentication

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-02-20 DOI: 10.1109/TIFS.2025.3544029

Dacan Luo;Junduan Huang;Weili Yang;M. Saad Shakeel;Wenxiong Kang

{"title":"RSNet: Region-Specific Network for Contactless Palm Vein Authentication","authors":"Dacan Luo;Junduan Huang;Weili Yang;M. Saad Shakeel;Wenxiong Kang","doi":"10.1109/TIFS.2025.3544029","DOIUrl":"10.1109/TIFS.2025.3544029","url":null,"abstract":"More palm features, such as veins and shapes obtained from an enlarged contactless palm vein region of interest (ROI), have been shown to improve recognition performance. However, a few efforts have been made to adequately utilize these features for mining identity information. To address this issue, we propose a Region-Specific Network (RSNet) for contactless palm vein authentication. Our RSNet is a dual-branch structure for global and local feature extraction. Firstly, a Region-based Local feature Enhancement Block (RLEB) is proposed at the local branch to extract region-specific features. In the RLEB, the intermediate feature maps are divided into three asymmetrical patches based on the physiological characteristics of palm vein and palm shape for extracting diversified features, enhancing the local feature representation. Then, a Multi-scale Aggregation Block (MAB) is proposed that efficiently aggregates multi-scale features at a more granular level. Furthermore, to guide the global and local branches in learning complementary feature aspects, a difference loss is introduced to apply a soft subspace orthogonality constraint between the global and local vectors during training. The global branch is designed to assist the learning process of local features, without being adopted for inference. Extensive experiments have demonstrated the effectiveness and superiority of our method, and the RSNet achieves new State-Of-The-Art (SOTA) authentication performance on seven public contactless palm vein databases in the open-set scenario.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2734-2747"},"PeriodicalIF":6.3,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462595","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