Covered Style Mining via Generative Adversarial Networks for Face Anti-spoofing

Highlights：

• A novel framework, CSM-GAN, is proposed to achieve face anti-spoofing using style transfer technology. CSM-GAN converts the original binary classification task into a style transfer task and, unlike existing methods, CSM-GAN mines potential difference distributions without introducing prior information and generates a difference map.

• To achieve end-to-end training and style transfer, an updatable three-channel difference map is designed to combine each sub-module, which provides richer photography style information than traditional fixed one-dimensional vectors.

• To prove the effectiveness of proposed method, extensive experiments are conducted on published face anti-spoofing datasets, demonstrating its superior performance to current stateof- the-art.