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For enterprise, personal and societal applications, there is now anincreasing demand for automated authentication of identity from images usingcomputer vision. However, current authentication technologies are stillvulnerable to presentation attacks. We present RoPAD, an end-to-end deeplearning model for presentation attack detection that employs unsupervisedadversarial invariance to ignore visual distractors in images for increasedrobustness and reduced overfitting. Experiments show that the proposedframework exhibits state-of-the-art performance on presentation attackdetection on several benchmark datasets.

RoPAD: Robust Presentation Attack Detection through Unsupervised Adversarial Invariance