  Recent advancements in domain generalization (DG) for face anti-spoofing
(FAS) have garnered considerable attention. Traditional methods have focused on
designing learning objectives and additional modules to isolate domain-specific
features while retaining domain-invariant characteristics in their
representations. However, such approaches often lack guarantees of consistent
maintenance of domain-invariant features or the complete removal of
domain-specific features. Furthermore, most prior works of DG for FAS do not
ensure convergence to a local flat minimum, which has been shown to be
advantageous for DG. In this paper, we introduce GAC-FAS, a novel learning
objective that encourages the model to converge towards an optimal flat minimum
without necessitating additional learning modules. Unlike conventional
sharpness-aware minimizers, GAC-FAS identifies ascending points for each domain
and regulates the generalization gradient updates at these points to align
coherently with empirical risk minimization (ERM) gradient updates. This unique
approach specifically guides the model to be robust against domain shifts. We
demonstrate the efficacy of GAC-FAS through rigorous testing on challenging
cross-domain FAS datasets, where it establishes state-of-the-art performance.
The code is available at https://github.com/leminhbinh0209/CVPR24-FAS.
