CLIP-SA: CLIP-Guided Semantic Alignment for Generalizable Face Anti-spoofing

Domain Generalization of Face Anti-Spoofing (DG-FAS) is increasingly becoming an important basis for model applications. Previous methods usually endeavor to project a domain-invariant feature space, which may not necessarily exist or be extremely difficult to explore. In this work, borrowing a solution from the large-scale vision-language models (VLMs) to replace direct editing of visual features, we propose a novel CLIP-Guided Semantic Alignment (CLIP-SA) for DG-FAS, which can guide visual feature alignment for generalization by leveraging the smoothness of the dense space of textual features. Specifically, in the textual branch, we design a Fine-Grained Prompt Template (FGPT) consisting of two language concepts, i.e., content and category, as supplements to visual features. Thus, the generalization of visual features can be improved by enhancing the model's understanding and association of content and category semantics. In the image encoder, we introduce a learnable Domain-Aware Adapter (DA-Adapter), which acts as a parameter-efficient transfer learning (PETL) method to transfer the pre-trained CLIP for the FAS task. Furthermore, the DA-Adapter contains an attention-based pooling layer that aggregates domain-invariant semantics as adaptive features for the output visual features. Finally, Our CLIP-SA demonstrates strong robustness to physical attacks on cross-domain benchmarks (ICMO, SCW), while also achieving excellent generalization against digital adversarial perturbations and on multiple Deepfake datasets.