Diversified Style Generation for Face Anti-Spoofing

Face Anti-Spoofing FAS is crucial for protecting face recognition systems against various spoofing attacks. However, existing methods still suffer from significant performance degradation when handling unseen domains. To ad-dress this challenge, this paper designs a Diversified Style Transformation Network DSTN that enhances the domain generalization capability of FAS models through instance-level style augmentation. At the core is a method called Diversified Style Generation DSG . DSG introduces a set of learnable style bases and uses the Dirichlet distribution to generate dynamic weights for each sample, constructing diversified style-enhanced features. During training, the model is exposed to a broader range of style variations, thereby learning style-invariant features. In addition, this paper designs a content consistency loss and a style diversity loss to preserve semantic information in the augment-ed features and to encourage diversity among style bases, further improving model robustness. Experiments on multiple standard cross-domain FAS benchmark datasets show that the proposed method outperforms state-of-the-art approaches across various domains, especially in unseen domain tasks, demonstrating stronger generalization capabilities. These results verify the ef-fectiveness and potential of DSG in solving the domain generalization prob-lem.