  Prior studies show that the key to face anti-spoofing lies in the subtle
image pattern, termed "spoof trace", e.g., color distortion, 3D mask edge,
Moire pattern, and many others. Designing a generic anti-spoofing model to
estimate those spoof traces can improve not only the generalization of the
spoof detection, but also the interpretability of the model's decision. Yet,
this is a challenging task due to the diversity of spoof types and the lack of
ground truth in spoof traces. This work designs a novel adversarial learning
framework to disentangle the spoof traces from input faces as a hierarchical
combination of patterns at multiple scales. With the disentangled spoof traces,
we unveil the live counterpart of the original spoof face, and further
synthesize realistic new spoof faces after a proper geometric correction. Our
method demonstrates superior spoof detection performance on both seen and
unseen spoof scenarios while providing visually convincing estimation of spoof
traces. Code is available at https://github.com/yaojieliu/ECCV20-STDN.

Computer Vision and Pattern Recognition