  Face anti-spoofing is essential to prevent false facial verification by using
a photo, video, mask, or a different substitute for an authorized person's
face. Most of the state-of-the-art presentation attack detection (PAD) systems
suffer from overfitting, where they achieve near-perfect scores on a single
dataset but fail on a different dataset with more realistic data. This problem
drives researchers to develop models that perform well under real-world
conditions. This is an especially challenging problem for frame-based
presentation attack detection systems that use convolutional neural networks
(CNN). To this end, we propose a new PAD approach, which combines pixel-wise
binary supervision with patch-based CNN. We believe that training a CNN with
face patches allows the model to distinguish spoofs without learning background
or dataset-specific traces. We tested the proposed method both on the standard
benchmark datasets -- Replay-Mobile, OULU-NPU -- and on a real-world dataset.
The proposed approach shows its superiority on challenging experimental setups.
Namely, it achieves higher performance on OULU-NPU protocol 3, 4 and on
inter-dataset real-world experiments.
