Sociology

Mathematics

Social science

Domain (mathematical analysis)

Politics

Geodesy

Machine learning

Liveness

Mathematical analysis

Glyph (data visualization)

Political science

Pairwise comparison

Benchmark (surveying)

Artificial intelligence

Geography

Spoofing attack

Generalization

Representation (politics)

Face (sociological concept)

Visualization

Computer science

Theoretical computer science

Computer security

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.

Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing