Deep Learning Approach for Secure and Trustworthy Biometric System
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 January 2025 | Viewed by 10783
Special Issue Editors
Interests: face anti-spoofing; rPPG; biometrics; AI security; video understanding; computer vision; machine learning
Interests: meta-learning; adversarial attack; robustness; face anti-spoofing; continual learning
Interests: image forensics; biometrics; computer vision; machine learning
Interests: visual computing; image search; image recognition
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Biometrics has been widely used in many personal and enterprise application systems, such as facial payments and video surveillance. Biologically unique identifiers, such as the face, fingerprints, iris, palms and veins, gait, voice, physiological signals, etc., appear reliable. However, the biometric system encounters various security challenges. In particular, recently emerging techniques have enabled realistic digital attacks with manipulation tools (e.g., Deepfake), high-fidelity physical presentation attacks (e.g., print, replay, 3D mask, and makeup), and adversarial attacks with imperceptible perturbations to humans. The growing prevalence of misinformation related to such falsified personally identifiable information has heightened interest in secure and trustworthy biometric systems for the AI community.
Topics of interest include but are not limited to:
- Attack detection for a wide range of biometrics (not limited to face, fingerprint, iris, palm print, gait, voice, biosignals, or remote photoplethysmography (rPPG));
- Novel deep learning approaches for face spoofing, forgery, and morphing detection;
- Adversarial attacks and backdoor attacks, as well as their defenses in biometrics;
- Deep learning for document liveness and recapturing detection;
- Analysis of robustness, generalization, and interpretability in biometric systems;
- Learning with fewer labels in biometric systems;
- Open-world biometric systems under unseen domains and unknown attacks;
- Privacy-preserving based deep learning for biometric systems;
- Review, survey, and new datasets on unimodal and multi-modal biometric systems.
Dr. Zitong Yu
Dr. Yunxiao Qin
Dr. Changsheng Chen
Dr. Zhaoqiang Xia
Dr. Zuheng Ming
Guest Editors
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Keywords
- face anti-spoofing
- deepfake detection
- face forgery detection
- adversarial attack
- robustness
- biometrics
- security and privacy
- computer vision
- deep learning
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