Attacking Face Recognition With T-Shirts: Database, Vulnerability Assessment, and Detection

Abstract

Face recognition systems are widely deployed for biometric authentication. Despite this, it is well-known that, without any safeguards, face recognition systems are highly vulnerable to presentation attacks. In response to this security issue, several promising methods for detecting presentation attacks have been proposed which show high performance on existing benchmarks. However, an ongoing challenge is the generalization of presentation attack detection methods to unseen and new attack types. To this end, we propose a new T-shirt Face Presentation Attack (TFPA) database of 1,608 T-shirt attacks using 100 unique presentation attack instruments. In an extensive evaluation, we show that this type of attack can compromise the security of face recognition systems and that some state-of-the-art attack detection mechanisms trained on popular benchmarks fail to robustly generalize to the new attacks. Further, we propose three new methods for detecting T-shirt attack images, one which relies on the statistical differences between depth maps of bona fide images and T-shirt attacks, an anomaly detection approach trained on features only extracted from bona fide RGB images, and a fusion approach which achieves competitive detection performance.

KRT-FUAP: Key Regions Tuned via Flow Field for Facial Universal Adversarial Perturbation

“…Adv-hat [31] utilized a printed sticker with a pattern that is applied to a hat to attack face recognition models. Ibsen et al [32] printed specially crafted facial images on T-shirts to confuse face recognition systems. Digital domain attacks involve directly modifying digital images using computer programs.…”

An In Depth Survey on Biometric ATM Handling through Fingerprint and Face Recognition using Deep Learning

“…Additionally, the system will think about using face recognition-based deep learning with sparse representation. M. Ibsen et al states in [2] that with the development of deep learning and the availability of big face databases, face recognition has become more common. In spite of this, it is evident that these systems are susceptible to presentation assaults, which need for strong detection techniques in order to prevent a facial recognition system's security from being compromised.…”