Detection of T-shirt Presentation Attacks in Face Recognition Systems

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.

Key Contributions

TFPA database with 1,608 T-shirt presentation attacks using 100 unique instruments
Demonstration that T-shirt attacks can compromise face recognition security
Spatial consistency detection method combining face and person detectors to detect T-shirt attacks

🛡️ Threat Analysis

Input Manipulation Attack

T-shirt attacks are physical presentation attacks (adversarial inputs) designed to cause face recognition misclassification at inference time. The paper demonstrates the attack and proposes a spatial consistency detection defense.