Purify then Guide: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing

Face anti-spoofing (FAS) is critical for secure deploymentof face recognition in high-stakes applications. However, existing multi-modal FAS methods still generalize poorly to unseen domains. We arguethat a key reason is that they directly align noisy multimodal featuresin which spoof-relevant cues are entangled with modality- and domain-specific variations, so both useful and nuisance patterns are forced tomatch across domains. Motivated by this, we design Multimodal Denoisingand Alignment (MMDA), a CLIP-based framework that (i) purifiesmultimodal features, (ii) softly guides them into a semantic space, and(iii) preserves the pre-trained geometry during task adaptation. To de-couple spoof cues from these variations before alignment, the Modality-Domain Joint Differential Attention (MD2A) module contrasts same-domain cross-sample features to down-weight patterns shared withineach domain/modality, which helps reduce domain- and modality-relatedbiases while better preserving discriminative spoof information. To avoiddestructive, over-rigid alignment, the Representation Space Soft (RS2)strategy aligns visual features to real/spoof subspaces spanned by mul-tiple prompts in the CLIP space, providing a soft semantic pull insteadof collapsing features onto a single text embedding. To prevent task-specific fine-tuning from destroying CLIP’s generalizable structure, theU-shaped Dual Space Adaptation (U-DSA) module introduces deepyet parameter-efficient adaptation and remaps adapted features backto shallower, more domain-invariant layers. Extensive experiments onWMCA, CeFA, PADISI, and SURF under complete-modality, missing-modality, and limited-source protocols show that MMDA consistentlysurpasses state-of-the-art methods, reducing average HTER by up to9.63% and improving AUC by up to 5.98% in cross-domain evaluation.