Domain-Generalized Face Anti-Spoofing with Unknown Attacks
This addresses a critical real-world challenge in biometric security by improving robustness to unseen domains and attacks, though it appears incremental as it builds on existing FAS methods.
The paper tackles the problem of face anti-spoofing under domain changes and unknown attacks, introducing DGUA-FAS with a Transformer-based feature extractor and synthetic unknown attack sample generator, achieving superior performance in domain generalization scenarios.
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.