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’.

中文翻译：

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对抗性活体检测器：在指纹活体检测中利用对抗性扰动

指纹认证系统 (FAS) 在消费电子产品中的广泛使用为高级演示攻击的发展打开了大门，即旨在使用伪造指纹绕过 FAS 的程序。因此，FAS 通常配备指纹呈现攻击检测 (FPAD) 模块，以从假冒副本中识别活指纹。在这项工作中，提出了一种基于卷积神经网络 (CNN) 和旨在迭代增加所考虑的检测器鲁棒性的临时对抗性数据增强策略的新型 FPAD 方法。特别是，对抗性指纹的概念，即通过使用临时指纹对抗性扰动算法伪装的假指纹，被用来帮助检测器只关注指纹的显着部分。该程序可以适应不同的 CNN、对抗性指纹算法和指纹扫描仪，使所提出的方法具有通用性，并且易于定制以适应不同的工作场景。为了测试所提出方法的有效性，作者参加了 LivDet 2021 竞赛，这是一项国际挑战赛，聚集了专家们在不同扫描仪和假副本生成方法下进行指纹活体检测，在“Liveness”的 23 名参与者中获得第一名在动作轨道中检测'。