Domain Generalization for Face Anti-spoofing via Content-aware Composite Prompt Engineering

• URL: http://arxiv.org/abs/2504.04470v1
• Date: Sun, 06 Apr 2025 13:00:41 GMT
• Title: Domain Generalization for Face Anti-spoofing via Content-aware Composite Prompt Engineering
• Authors: Jiabao Guo, Ajian Liu, Yunfeng Diao, Jin Zhang, Hui Ma, Bo Zhao, Richang Hong, Meng Wang,
• Abstract summary: Domain Generalization in Face Anti-Spoofing (FAS) is the significant interference of domain-specific signals on subtle spoofing clues.<n>We propose a novel Content-aware Composite Prompt Engineering (CCPE) that generates instance-wise composite prompts.<n>Our CCPE has been validated for its effectiveness in multiple cross-domain experiments and achieves state-of-the-art (SOTA) results.
• Score: 38.82454563769887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The challenge of Domain Generalization (DG) in Face Anti-Spoofing (FAS) is the significant interference of domain-specific signals on subtle spoofing clues. Recently, some CLIP-based algorithms have been developed to alleviate this interference by adjusting the weights of visual classifiers. However, our analysis of this class-wise prompt engineering suffers from two shortcomings for DG FAS: (1) The categories of facial categories, such as real or spoof, have no semantics for the CLIP model, making it difficult to learn accurate category descriptions. (2) A single form of prompt cannot portray the various types of spoofing. In this work, instead of class-wise prompts, we propose a novel Content-aware Composite Prompt Engineering (CCPE) that generates instance-wise composite prompts, including both fixed template and learnable prompts. Specifically, our CCPE constructs content-aware prompts from two branches: (1) Inherent content prompt explicitly benefits from abundant transferred knowledge from the instruction-based Large Language Model (LLM). (2) Learnable content prompts implicitly extract the most informative visual content via Q-Former. Moreover, we design a Cross-Modal Guidance Module (CGM) that dynamically adjusts unimodal features for fusion to achieve better generalized FAS. Finally, our CCPE has been validated for its effectiveness in multiple cross-domain experiments and achieves state-of-the-art (SOTA) results.

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