Abstract
Face anti-spoofing is designed to prevent face recognition systems from recognizing fake faces as the genuine
users. While advanced face anti-spoofing methods are developed, new types of spoof attacks are also being created
and becoming a threat to all existing systems. We define
the detection of unknown spoof attacks as Zero-Shot Face
Anti-spoofing (ZSFA). Previous ZSFA works only study 1- 2 types of spoof attacks, such as print/replay, which limits
the insight of this problem. In this work, we investigate the
ZSFA problem in a wide range of 13 types of spoof attacks,
including print, replay, 3D mask, and so on. A novel Deep
Tree Network (DTN) is proposed to partition the spoof samples into semantic sub-groups in an unsupervised fashion.
When a data sample arrives, being know or unknown attacks, DTN routes it to the most similar spoof cluster, and
makes the binary decision. In addition, to enable the study
of ZSFA, we introduce the first face anti-spoofing database
that contains diverse types of spoof attacks. Experiments
show that our proposed method achieves the state of the art
on multiple testing protocols of ZSFA.