VADAPT: Visual Anchored and Dual-Prompt Tuning for Generalizable Face Attack Detection

Generalizable face attack detection must handle not only conventional physical presentation attacks but also rapidly evolving digital manipulations. While pretrained vision-language models (VLMs) offer transferable representations, their direct adaptation to this task remains limited by heterogeneous real/fake distributions and concentrated reliance on a narrow set of visual evidence. To address these limitations, we propose VADAPT, a Visual Anchored and Dual-Prompt Tuning framework for generalizable face attack detection. VADAPT introduces Visual Soft-Anchoring, which uses indexed textual anchors to softly differentiate multiple learnable visual part tokens, encouraging diverse fine-grained evidence exploration without predefined anatomical partitions. In parallel, Dual-Branch Latent Context learns multiple continuous contexts to capture heterogeneous genuine and attack-related representations, while Asymmetric Feature Aggregation adaptively combines their responses using branch-specific aggregation rules. Extensive experiments on UniAttackData and HydraFake demonstrate strong cross-attack generalization. VADAPT achieves state-of-the-art performance on UniAttackData under the official cross-form protocol, attaining 13.45% ACER, compared with 15.13% for the strongest previous baseline.