Detection of T-shirt Presentation Attacks in Face Recognition Systems
For face recognition system developers, this work highlights a novel attack vector and provides a detection method, but the approach is incremental as it combines existing detectors.
This work introduces a new type of presentation attack using T-shirts and shows that it can compromise face recognition systems. The proposed detection method, based on spatial consistency checks between face and person detectors, achieves reliable detection on a dataset of 1,608 T-shirt attacks and 152 bona fide presentations.
Face recognition systems are often used for biometric authentication. Nevertheless, it is known that without any protective measures, face recognition systems are vulnerable to presentation attacks. To tackle this security problem, methods for detecting presentation attacks have been developed and shown good detection performance on several benchmark datasets. However, generalising presentation attack detection methods to new and novel types of attacks is an ongoing challenge. In this work, we employ 1,608 T-shirt attacks of the T-shirt Face Presentation Attack (TFPA) database using 100 unique presentation attack instruments together with 152 bona fide presentations. In a comprehensive evaluation, we show that this type of attack can compromise the security of face recognition systems. Furthermore, we propose a detection method based on spatial consistency checks in order to detect said T-shirt attacks. Precisely, state-of-the-art face and person detectors are combined to analyse the spatial positions of detected faces and persons based on which T-shirt attacks can be reliably detected.