Face Anti-Spoofing (FAS) research is challenged by the cross-domain problem, where there is a domain gap between the training and testing data. While recent FAS works are mainly model-centric, focusing on developing domain generalization algorithms for improving cross-domain performance, data-centric research for face anti-spoofing, improving generalization from data quality and quantity, is largely ignored. Therefore, our work starts with data-centric FAS by conducting a comprehensive investigation from the data perspective for improving cross-domain generalization of FAS models. More specifically, at first, based on physical procedures of capturing and recapturing, we propose task-specific FAS data augmentation (FAS-Aug), which increases data diversity by synthesizing data of artifacts, such as printing noise, color distortion, moir\'e pattern, \textit{etc}. Our experiments show that using our FAS augmentation can surpass traditional image augmentation in training FAS models to achieve better cross-domain performance. Nevertheless, we observe that models may rely on the augmented artifacts, which are not environment-invariant, and using FAS-Aug may have a negative effect. As such, we propose Spoofing Attack Risk Equalization (SARE) to prevent models from relying on certain types of artifacts and improve the generalization performance. Last but not least, our proposed FAS-Aug and SARE with recent Vision Transformer backbones can achieve state-of-the-art performance on the FAS cross-domain generalization protocols. The implementation is available at https://github.com/RizhaoCai/FAS_Aug.

本研究针对面部防伪中的跨域问题，特别关注数据质量和数量的影响。我们提出了任务特定的面部防伪数据增强方法（FAS-Aug），通过合成各种伪造物的噪声，提升数据多样性，从而优化模型的跨域性能。此外，我们引入了防伪攻击风险均衡（SARE）策略，进一步提高泛化能力。最后，结合最新的视觉变换器网络，我们的方法在跨域泛化领域取得了最先进的性能。

面向数据中心的面部防伪：通过基于物理的数据合成改善跨域泛化