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Recent face presentation attack detection (PAD) leverages domain adaptation(DA) and domain generalization (DG) techniques to address performancedegradation on unknown domains. However, DA-based PAD methods require access tounlabeled target data, while most DG-based PAD solutions rely on a priori,i.e., known domain labels. Moreover, most DA-/DG-based methods arecomputationally intensive, demanding complex model architectures and/ormulti-stage training processes. This paper proposes to model face PAD as acompound DG task from a causal perspective, linking it to model optimization.We excavate the causal factors hidden in the high-level representation viacounterfactual intervention. Moreover, we introduce a class-guided MixStyle toenrich feature-level data distribution within classes instead of focusing ondomain information. Both class-guided MixStyle and counterfactual interventioncomponents introduce no extra trainable parameters and negligible computationalresources. Extensive cross-dataset and analytic experiments demonstrate theeffectiveness and efficiency of our method compared to state-of-the-art PADs.The implementation and the trained weights are publicly available.

Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding Statistics