Bottom-Up Domain Prompt Tuning for Generalized Face Anti-Spoofing

Face anti-spoofing (FAS) which plays an important role in securing face recognition systems has been attracting increasing attention. Recently, the popular vision-language model CLIP has been proven to be effective for FAS, where outstanding performance can be achieved by simply transferring the class label into textual prompt. In this work, we aim to improve the generalization ability of CLIP-based FAS from a prompt learning perspective. Specifically, a Bottom-Up Domain Prompt Tuning method (BUDoPT) that covers the different levels of domain variance, including the domain of recording settings and domain of attack types is proposed. To handle domain discrepancies of recording settings, we design a context-aware adversarial domain-generalized prompt learning strategy that can learn domain-invariant prompt. For the spoofing domain with different attack types, we construct a fine-grained textual prompt that guides CLIP to look through the subtle details of different attack instruments. Extensive experiments are conducted on five FAS datasets with a large number of variations (camera types, resolutions, image qualities, lighting conditions, and recording environments). The effectiveness of our proposed method is evaluated with different amounts of source domains from multiple angles, where we boost the generalizability compared with the state of the arts with multiple training datasets or with only one dataset.