  Face anti-spoofing has drawn a lot of attention due to the high security
requirements in biometric authentication systems. Bringing face biometric to
commercial hardware became mostly dependent on developing reliable methods for
detecting fake login sessions without specialized sensors. Current CNN-based
method perform well on the domains they were trained for, but often show poor
generalization on previously unseen datasets. In this paper we describe a
method for utilizing unsupervised pretraining for improving performance across
multiple datasets without any adaptation, introduce the Entry Antispoofing
Dataset for supervised fine-tuning, and propose a multi-class auxiliary
classification layer for augmenting the binary classification task of detecting
spoofing attempts with explicit interpretable signals. We demonstrate the
efficiency of our model by achieving state-of-the-art results on cross-dataset
testing on MSU-MFSD, Replay-Attack, and OULU-NPU datasets.
