Semantically Anchored Test-Time Domain Generalization for Face Anti-Spoofing

Abstract

: To ensure the reliability of biometric authentication, Face Anti-Spoofing (FAS) models must accurately detect presentation attacks. However, due to the highly complex distribution shifts caused by variations in style, cross-domain generalization remains a significant challenge. Test-Time Domain Generalization (TTDG) has recently surfaced as an innovative framework, facilitating the adaptation of unseen samples to source-domain characteristics through the strategic utilization of learned style bases. Nevertheless, existing TTDG methods optimize randomly initialized style bases solely through statistical objectives, leaving a critical research gap: the lack of explicit semantic constraints inevitably leads to hierarchical semantic inconsistency and weakens subtle spoofing cues. To fill this gap, we introduce the Semantic-Anchored Test-Time Domain Generalization (SA-TTDG) framework. The core novelty of our approach lies in introducing a Text-Anchored Style Projection (TASP), which utilizes rich linguistic priors from Vision-Language Models (VLMs) to initialize and strongly constrain learnable style bases. By anchoring these bases to explicit semantic concepts, TASP encourages semantic consistency across hierarchical feature representations. Furthermore, to fully exploit these semantically aligned style representations, we design a Semantic Prompt Modulation (SPM) module driven by a Style-Query Cross-Attention (SQ-CA) mechanism. Instead of using static queries, SPM dynamically retrieves multi-level style cues to generate domain-sensitive prompts, which effectively modulate the original visual features. This process helps enhance subtle spoofing-related cues while preserving the underlying content structure. Evaluations under standard leave-one-domain-out protocols demonstrate that the proposed framework consistently reduces cross-domain classification errors compared to existing statistical TTDG baselines.