  Due to their convenience and high accuracy, face recognition systems are
widely employed in governmental and personal security applications to
automatically recognise individuals. Despite recent advances, face recognition
systems have shown to be particularly vulnerable to identity attacks (i.e.,
digital manipulations and attack presentations). Identity attacks pose a big
security threat as they can be used to gain unauthorised access and spread
misinformation. In this context, most algorithms for detecting identity attacks
generalise poorly to attack types that are unknown at training time. To tackle
this problem, we introduce a differential anomaly detection framework in which
deep face embeddings are first extracted from pairs of images (i.e., reference
and probe) and then combined for identity attack detection. The experimental
evaluation conducted over several databases shows a high generalisation
capability of the proposed method for detecting unknown attacks in both the
digital and physical domains.
