  With diverse presentation attacks emerging continually, generalizable face
anti-spoofing (FAS) has drawn growing attention. Most existing methods
implement domain generalization (DG) on the complete representations. However,
different image statistics may have unique properties for the FAS tasks. In
this work, we separate the complete representation into content and style ones.
A novel Shuffled Style Assembly Network (SSAN) is proposed to extract and
reassemble different content and style features for a stylized feature space.
Then, to obtain a generalized representation, a contrastive learning strategy
is developed to emphasize liveness-related style information while suppress the
domain-specific one. Finally, the representations of the correct assemblies are
used to distinguish between living and spoofing during the inferring. On the
other hand, despite the decent performance, there still exists a gap between
academia and industry, due to the difference in data quantity and distribution.
Thus, a new large-scale benchmark for FAS is built up to further evaluate the
performance of algorithms in reality. Both qualitative and quantitative results
on existing and proposed benchmarks demonstrate the effectiveness of our
methods. The codes will be available at https://github.com/wangzhuo2019/SSAN.

Computer Vision and Pattern Recognition