IET Biometrics最新文献

{"title":"Multibranch Collaboration and Segmented Training Network for Image Forgery Comprehensive Detection","authors":"Jing Chang,&nbsp;Jixiang Yang,&nbsp;Zuofan Gan,&nbsp;Lianjin Guo","doi":"10.1049/bme2/1389267","DOIUrl":"10.1049/bme2/1389267","url":null,"abstract":"<p>With the rise of sophisticated image manipulation techniques, image detection forgeries have become increasingly challenging. This paper presents a novel deep learning framework, the multibranch collaboration and segmented training network (MBC-STN), for robust image forgery detection. MBC-STN addresses the challenges posed by diverse forgery types and complex noisy environments through a multibranch collaborative architecture that integrates three specialized branches: the forgery edge-aware branch (FEB), the noise-aware branch (NB), and the color-aware branch (CB). These branches capture image forgery features from multiple dimensions, significantly enhancing the precision and robustness of forgery detection. Additionally, MBC-STN employs a segmented training strategy to optimize the performance of different branches in stages, improving training efficiency, and model adaptability. Three loss functions (balanced binary cross-entropy loss, balanced dice loss, and binary cross-entropy loss) are employed to guide MBC-STN in learning manipulated traces and identifying forgery regions. Extensive experiments on multiple datasets demonstrate that MBC-STN outperforms state-of-the-art methods maintain robustness under different postprocessing conditions.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2/1389267","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147315464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148067790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Usability Evaluation of a Push-Based Passwordless Authentication Model Using Public-Key Cryptography","authors":"Ghulam Mustafa,&nbsp;Shah Zaman Nizamani,&nbsp;Irfana Memon,&nbsp;Asif Ali Wagan,&nbsp;Solomiia Fedushko,&nbsp;Abdullah Al Hejaili","doi":"10.1049/bme2/5462893","DOIUrl":"10.1049/bme2/5462893","url":null,"abstract":"<p>Despite major advancements in the sphere of the public-key authentication specifically in the instances of the newly established standards like WebAuthn and the FIDO2, the practical implementation of the passwordless login systems is still hindered by the usability factors, platform-related requirements, and the very nature of the deployment process is predetermined by its complex character. We formulate in this contribution a practical, push-based authentication model, which takes active advantage of the utilization of the public-key cryptography, combined with the traditional set of capabilities in mobile devices, to provide a secure and consumer-friendly way of logging into a system. The architecture has been implemented using the Laravel web application framework, the Flutter cross-platform mobile development system, and Firebase Cloud Messaging (FCM), thus enabling authentication using smartphone-initiated approvals and digital signatures. A within-subject usability experiment with 160 participants was the measure of effectiveness of the proposed approach. The participants were requested to fulfill the tasks of logins with the postulated push-based mechanism and an experimental password-based one. System logs provided objective metrics such as the latency to log in, rate of errors, and the success rate of the first attempt. At the same time, the subjective ratings were gathered through the means of structured questionnaires where the ease of use, speed, security, and preferences were measured. Empirical data revealed that despite the insignificant increase in time of login, the passwordless system reported a significant decrease in error, increase in success rate, and a significant preference among the subjects in all the dimensions of usability that were being tested. Each of the observed differences was statistically significant and with large effect sizes. This research provides a deplorable, platform adaptable authentication paradigm that preempts realistic coping, yet holds core security standards. Emphasizing the need to balance system design with human-centered assessment the work suggests a possible solution to more complex or more hardware-resources limited passwordless usage.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2/5462893","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146162344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Unified Physical–Digital Face Attack Detection Challenge: A Review","authors":"Junze Zheng,&nbsp;Xinping Gao,&nbsp;Ajian Liu,&nbsp;Haocheng Yuan,&nbsp;Jun Wan,&nbsp;Yanyan Liang,&nbsp;Jiankang Deng,&nbsp;Sergio Escalera,&nbsp;Hugo Jair Escalante,&nbsp;Zhen Lei,&nbsp;Isabelle Guyon,&nbsp;Du Zhang","doi":"10.1049/bme2/9653627","DOIUrl":"10.1049/bme2/9653627","url":null,"abstract":"<p>Face antispoofing (FAS) technologies play a pivotal role in safeguarding face recognition (FR) systems against potential security loopholes. The biometric community has witnessed significant advancements lately, largely due to the exceptional performance of deep learning architectures and the abundance of substantial datasets. Despite these progress, FR systems remain susceptible to both physical and digital forgery attacks. However, most existing detection methods do not address both types of threats concurrently. To bridge this gap and foster the development of a comprehensive detection framework, we have compiled a unified dataset named UniAttackData. This dataset incorporates both physical and digital spoofing attacks while maintaining identity consistency, encompassing 1800 participants each subjected to two different physical attacks (PAs) and 12 different digital attacks (DAs), respectively. This effort has resulted in a comprehensive collection of 29,706 video samples. We organized the Chalearn FAS face attack detection challenge based on this novel resource to boost research aiming to promote joint antispoofing efforts. The Chalearn unified antispoofing attack detection challenge drew 136 teams during the development phase, with 13 teams advancing to the final round. The organizing team revalidated and re-executed the submitted code to determine the final rankings. This paper provides a summary of the challenge, covering the dataset used, the protocol definition, the evaluation metrics, and the competition results. Additionally, we discuss the top-ranked algorithms and the research insights offered by the competition for attack detection.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2/9653627","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148064503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148098826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Robustness Analysis of Distributed CNN Model Training in Expression Recognition","authors":"Jun Li","doi":"10.1049/bme2/4107824","DOIUrl":"10.1049/bme2/4107824","url":null,"abstract":"<p>Facial expression recognition is vital in pattern recognition and affective computing. With the advancement of deep learning, its performance has improved, yet challenges remain in nonlaboratory environments due to occlusion, poor lighting, and varying head poses. This study explores a robust facial expression recognition approach using a CNN-based model integrated with key point localization techniques. Instead of relying on a dense set of landmarks, the proposed method focuses on fewer but more informative expression key points. Each point is analyzed for local shape features, and contour consistency is verified using indexing along the normal direction. This strategy enhances robustness while reducing computational complexity. Specifically, the hybrid active shape model (ASM) + structure method significantly lowers the processing load compared to the traditional ASM approach. Experimental results demonstrate a 3.02% improvement in recognition accuracy over one-to-many SVM classifiers when dealing with clear facial images. Furthermore, the system shows strong resilience to partial occlusions and maintains real-time performance, making it suitable for real-world applications. The proposed framework highlights the importance of selecting effective key points and optimizing feature extraction to enhance both accuracy and efficiency in facial expression recognition tasks under challenging conditions.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2/4107824","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146148306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148081769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Introducing Learnable Gaussian Noise Into Defed for Enhanced Defense Against Adversarial Attacks in Fingerprint Liveness Detection","authors":"Shuifa Sun,&nbsp;Shaohua Hu,&nbsp;Yifei Wang,&nbsp;Wanyi Zheng,&nbsp;Jianming Lin,&nbsp;Sani M. Abdullahi,&nbsp;Jian Zhang","doi":"10.1049/bme2/5664546","DOIUrl":"10.1049/bme2/5664546","url":null,"abstract":"<p>Deep learning has significantly improved the performance of fingerprint liveness detection, while susceptibility to adversarial attacks remains a critical security challenge. Existing input transformation–based defense methods, including JPEG compression, total variance minimization (TVM), high-level representation guided denoiser (HGD), and Defed, are typically designed for specific attacks, resulting in limited generalization across diverse adversarial scenarios. Experimental analysis indicates that among the four defense methods based on input transformation, Defed achieves the best overall performance when evaluated against both momentum iterative fast gradient sign method (MI-FGSM) and DeepFool attacks. However, Defed exhibits strong robustness against MI-FGSM attacks but demonstrates insufficient defense effectiveness against DeepFool attacks. To address this issue, an improved method of Defed has been proposed by integrating a learnable Gaussian noise module into the core structure to enable adaptive suppression of adversarial perturbations, and by employing 1 × 1 convolutions to allow cross-channel information interaction, thereby enhancing feature consistency and overall robustness. Experimental results on the LivDet 2015 dataset demonstrate that the defense success rate against DeepFool attacks has increased by 3%–5%, while strong robustness against MI-FGSM attacks has been maintained, substantially improving the security and reliability of fingerprint liveness detection systems.</p>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2/5664546","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146002409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148072893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}