Token-Wise Asymmetric Contrastive Learning in Countering Unknown Attacks for Face Anti-Spoofing

Abstract

Face anti-spoofing (FAS) techniques are crucial for safeguarding face recognition systems against an ever-evolving landscape of spoofing attacks. While existing methods have made strides in detecting known attacks, robustness against unknown, potentially more sophisticated attack types remains a critical and unresolved challenge in the field. In this paper, we propose a novel framework integrating token-wise asymmetric contrastive learning with angular margin loss to enhance the robustness of FAS models against unknown attack types. The key idea to resolve the problem is to learn a feature space where live face features are densely distributed, whereas spoof face features are more dispersed. This is achieved through two novel strategies: 1) asymmetric contrastive learning, encouraging the FAS model to learn a compact distribution for live face features while relaxing the constraint on the distribution of spoof face features, and 2) token-wise learning, focusing on capturing intrinsic liveness cues from local region rather than identity or facial-related features. Additionally, an angular margin loss is incorporated to enhance the discriminative power of the learned features. Extensive experiments on public benchmark datasets demonstrate the superiority of our FAS model over state-of-the-art methods in cross-attack scenarios, showcasing its strong robustness to unknown attacks while maintaining unseen domain generalization capability.