Towards Open-set Face Anti-spoofing with Unseen Attack Synthesis

Existing face anti-spoofing (FAS) methods have primarily focused on close-set or cross-domain settings with a few pre-defined presentation attacks (PAs). However, with the continual emergence of diverse PAs, we argue that developing a generalizable FAS detector for unseen PAs deserves more attention from the FAS community. In this work, we investigate the open-set FAS setting, where the spoof types in testing are unseen. The key challenge is that the unseen PAs are designed to deceive the FAS model and thus appear similar to live faces both at the pixel level and in the feature space. To address this issue, we propose a novel framework for synthesizing unseen PAs and pushing the generated samples toward an open category space. Our approach is motivated by empirical findings that unseen PAs are more likely to be compactly clustered by spoof type and located at the boundary of the live distribution in the spoof-type-aware feature space derived from multi-class optimization. Lastly, we evaluate our method on the SiW-Mv2 cross-type benchmark using both fine-grained and coarse-grained protocols. Compared to the baselines and existing top competitors in close-set or cross-domain settings, our method outperforms them significantly on both protocols.