Adversarial liveness detector: Leveraging adversarial perturbations in fingerprint liveness detection

Abstract

Abstract The widespread use of fingerprint authentication systems (FASs) in consumer electronics opens for the development of advanced presentation attacks, that is, procedures designed to bypass a FAS using a forged fingerprint. As a consequence, FAS are often equipped with a fingerprint presentation attack detection (FPAD) module, to recognise live fingerprints from fake replicas. In this work, a novel FPAD approach based on Convolutional Neural Networks (CNNs) and on an ad hoc adversarial data augmentation strategy designed to iteratively increase the considered detector robustness is proposed. In particular, the concept of adversarial fingerprint, that is, fake fingerprints disguised by using ad hoc fingerprint adversarial perturbation algorithms was leveraged to help the detector focus only on salient portions of the fingerprints. The procedure can be adapted to different CNNs, adversarial fingerprint algorithms and fingerprint scanners, making the proposed approach versatile and easily customisable todifferent working scenarios. To test the effectiveness of the proposed approach, the authors took part in the LivDet 2021 competition, an international challenge gathering experts to compete on fingerprint liveness detection under different scanners and fake replica generation approach, achieving first place out of 23 participants in the ‘Liveness Detection in Action track’.

Keywords

• biometrics presentation attack detection
• learning (artificial intelligence)
• liveness detection