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Author: search.filters.author.Muangkan, RonnakornSubject: search.filters.subject.presentation attack detection

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    Item type:Publication,
    Cross-Domain Robust Liveness Detection: A Transfer Learning Approach for Combating Sophisticated Presentation Attacks in Mobile Authentication
    (2025-01-01)
    Kerdpramote, Phuvis
    ;
    Poomdeesittinon, Akeanant
    ;
    Jamsri, Rapeeploy
    ;
    Krueyos, Phanuwit
    ;
    Muangkan, Ronnakorn
    As biometric authentication systems become ubiquitous in Southeast Asia's digital economy, sophisticated presentation attacks using deepfakes, high-resolution displays, and 3D masks pose critical security threats. This paper presents a comprehensive cross-domain liveness detection framework that addresses the generalization challenges plaguing current systems. Our approach leverages MobileNetV2-based transfer learning with a novel two-phase training strategy, achieving superior cross-domain performance while maintaining computational efficiency for mobile deployment. We introduce domain-aware augmentation techniques and evaluate our system across multiple benchmark datasets including NUAA and a locally-collected Thai demographic dataset. Experimental results demonstrate 84.35% accuracy on NUAA and 78.62% cross-domain accuracy, with significant improvements in Attack Presentation Classification Error Rate (APCER) reduction from 28.7% to 15.4% compared to baseline methods. The system successfully detects emerging attack vectors including deepfake videos and tablet-based spoofing attempts. We provide comprehensive analysis of deployment challenges in resource-constrained environments demonstrating practical applicability for Thailand's mobile banking and digital identity verification ecosystem.