Towards Data-Centric Face Anti-Spoofing: Improving Cross-domain Generalization via Physics-based Data Synthesis
Face Anti-Spoofing (FAS) research is challenged by the cross-domain problem, where there is a domain gap between the training and testing data. While recent FAS works are mainly model-centric, focusing on developing domain generalization algorithms for improving cross-domain performance, data-centri...
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Zusammenfassung: | Face Anti-Spoofing (FAS) research is challenged by the cross-domain problem,
where there is a domain gap between the training and testing data. While recent
FAS works are mainly model-centric, focusing on developing domain
generalization algorithms for improving cross-domain performance, data-centric
research for face anti-spoofing, improving generalization from data quality and
quantity, is largely ignored. Therefore, our work starts with data-centric FAS
by conducting a comprehensive investigation from the data perspective for
improving cross-domain generalization of FAS models. More specifically, at
first, based on physical procedures of capturing and recapturing, we propose
task-specific FAS data augmentation (FAS-Aug), which increases data diversity
by synthesizing data of artifacts, such as printing noise, color distortion,
moir\'e pattern, \textit{etc}. Our experiments show that using our FAS
augmentation can surpass traditional image augmentation in training FAS models
to achieve better cross-domain performance. Nevertheless, we observe that
models may rely on the augmented artifacts, which are not
environment-invariant, and using FAS-Aug may have a negative effect. As such,
we propose Spoofing Attack Risk Equalization (SARE) to prevent models from
relying on certain types of artifacts and improve the generalization
performance. Last but not least, our proposed FAS-Aug and SARE with recent
Vision Transformer backbones can achieve state-of-the-art performance on the
FAS cross-domain generalization protocols. The implementation is available at
https://github.com/RizhaoCai/FAS_Aug. |
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DOI: | 10.48550/arxiv.2409.03501 |