  The vulnerability of face recognition systems to presentation attacks has
limited their application in security-critical scenarios. Automatic methods of
detecting such malicious attempts are essential for the safe use of facial
recognition technology. Although various methods have been suggested for
detecting such attacks, most of them over-fit the training set and fail in
generalizing to unseen attacks and environments. In this work, we use transfer
learning from the vision transformer model for the zero-shot anti-spoofing
task. The effectiveness of the proposed approach is demonstrated through
experiments in publicly available datasets. The proposed approach outperforms
the state-of-the-art methods in the zero-shot protocols in the HQ-WMCA and
SiW-M datasets by a large margin. Besides, the model achieves a significant
boost in cross-database performance as well.
