The growing availability of cheap and reliable fingerprint acquisition scanners is resulting in an increasing spread of Fingerprint-based Authentication Systems (FAS) in consumer electronics. This has giving rise to a new wave in research on both smarter spoofing attacks, aimed to bypass a FAS by using a counterfeit fingerprint, and on more effective Liveness Detectors (LD), aimed to discern authentic (live) fingerprints from fake ones. As in many other computer vision tasks, deep Convolutional Neural Networks (CNN) demonstrated to be very effective also for fingerprint liveness detection. However, we showed that it is possible to . In this paper, we want to make a step further toward the design of a black-box attack by . To this aim, we designed an attack scenario where a shadow LD (i.e. an adaptation of the substitute technique for the liveness detection application) is used to generate an adversarial fingerprint in a white-box setting before submitting it to the real target LD, invoked in a total back-box manner. Finally, we analysed the impact that such attack has on the authentication system, also analysing if and to what extent the scanner and the spoofing material combinations affect the success of the attack.

中文翻译：

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针对基于指纹的认证系统的对抗性扰动攻击的可转移性


廉价且可靠的指纹采集扫描仪的日益普及导致基于指纹的身份验证系统 (FAS) 在消费电子产品中的日益普及。这引发了新一波研究浪潮，包括更智能的欺骗攻击（旨在通过使用伪造指纹绕过 FAS）和更有效的活体检测器 (LD)（旨在辨别真（活）指纹和假指纹）。与许多其他计算机视觉任务一样，深度卷积神经网络 (CNN) 被证明对于指纹活体检测也非常有效。然而，我们证明了这是可能的。在本文中，我们希望在黑盒攻击的设计方面更进一步。为此，我们设计了一种攻击场景，其中使用影子 LD（即活体检测应用的替代技术的改编）在白盒设置中生成对抗性指纹，然后将其提交给真实目标 LD，调用以完全后箱的方式。最后，我们分析了此类攻击对身份验证系统的影响，还分析了扫描仪和欺骗材料组合是否以及在何种程度上影响攻击的成功。