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.

翻译：尽管人脸反欺骗（FAS）方法在特定域或攻击类型上取得了显著性能，但很少有研究关注域变化与未知攻击同时存在的情况，而这更接近真实应用场景。为处理域泛化的未知攻击，我们提出一种新方法DGUA-FAS，该方法包含基于Transformer的特征提取器和合成未知攻击样本生成器（SUASG）。SUASG网络通过模拟未知攻击样本来辅助特征提取器的训练。实验结果表明，我们的方法在面向已知或未知攻击的域泛化人脸反欺骗任务中达到了优越性能。