Self-Attention Generative Distribution Adversarial Network for Few- and Zero-Shot Face Anti-Spoofing

With the exponential growth of facial authentications, the face anti-spoofing area has come to play an indispensable role as a shield, protecting those systems against facial impostures. However, because most current anti-spoofing technologies work with type-specific supervision, they are only effective in their respective spoof types, which means they are unlikely to prove robust for unidentified attack forms that are beyond their predefined supervised limitations. With this point in mind, we herein propose a novel Adversarial Distribution Generative Network (ADGN) that extends its spatial attention to a comprehensive global context, thus extensively raising the level of generality for unknown cases that inherently provide few or even no clues with which to learn. In this paper, we are more in favor of speculating on 3D mask attacks, where a great scarcity of prior knowledge is virtually inevitable due to their prohibitive costs. We also demonstrate the resilience of our proposed model and test it against publicly available datasets on both seen and unseen spoof scenarios. This intends to show how our model provides competitive detecting performance against a wide range of spoof types in comparison with previous state-of-the-art methods.