  Face anti-spoofing aims to prevent false authentications of face recognition
systems by distinguishing whether an image is originated from a human face or a
spoof medium. We propose a novel method called Doubly Adversarial Suppression
Network (DASN) for domain-agnostic face anti-spoofing; DASN improves the
generalization ability to unseen domains by learning to effectively suppress
spoof-irrelevant factors (SiFs) (e.g., camera sensors, illuminations). To
achieve our goal, we introduce two types of adversarial learning schemes. In
the first adversarial learning scheme, multiple SiFs are suppressed by
deploying multiple discrimination heads that are trained against an encoder. In
the second adversarial learning scheme, each of the discrimination heads is
also adversarially trained to suppress a spoof factor, and the group of the
secondary spoof classifier and the encoder aims to intensify the spoof factor
by overcoming the suppression. We evaluate the proposed method on four public
benchmark datasets, and achieve remarkable evaluation results. The results
demonstrate the effectiveness of the proposed method.
