Domain-Generalized Face Anti-Spoofing with Unknown Attacks
Zong-Wei Hong Yu-Chen Lin Hsuan-Tung Liu Yi-Ren Yeh Chu-Song Chen
Although face anti-spoofing (FAS) methods have achieved remarkable
performance on specific domains or attack types, few studies have focused on
the simultaneous presence of domain changes and unknown attacks, which is
closer to real application scenarios. To handle domain-generalized unknown
attacks, we introduce a new method, DGUA-FAS, which consists of a
Transformer-based feature extractor and a synthetic unknown attack sample
generator (SUASG). The SUASG network simulates unknown attack samples to assist
the training of the feature extractor. Experimental results show that our
method achieves superior performance on domain generalization FAS with known or
unknown attacks.