Deep neural networks are vulnerable to adversarial samples, posing potential threats to the applications deployed with deep learning models in practical conditions. A typical example is the fingerprint liveness detection module in fingerprint authentication systems. Inspired by great progress of deep learning, deep networks-based fingerprint liveness detection algorithms spring up and dominate the field. Thus, we investigate the feasibility of deceiving state-of-the-art deep networks-based fingerprint liveness detection schemes by leveraging this property in this paper. Extensive evaluations are made with three existing adversarial methods: FGSM, MI-FGSM, and Deepfool. We also proposed an adversarial attack method that enhances the robustness of adversarial fingerprint images to various transformations like rotations and flip. We demonstrate these outstanding schemes are likely to classify fake fingerprints as live fingerprints by adding tiny perturbations, even without internal details of their used model. The experimental results reveal a big loophole and threats for these schemes from a view of security, and enough attention is urgently needed to be paid on anti-adversarial not only in fingerprint liveness detection but also in all deep learning applications.

中文翻译：

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对抗指纹活力检测

深度神经网络容易受到对抗性样本的攻击，在实际情况下会对部署有深度学习模型的应用程序构成潜在威胁。一个典型的例子是指纹认证系统中的指纹活跃度检测模块。在深度学习的巨大进步启发下，基于深度网络的指纹活跃度检测算法应运而生，并主导了该领域。因此，我们通过利用此属性，研究了基于最新的深层网络的指纹活动检测方案的可行性。使用三种现有的对抗方法进行了广泛的评估：FGSM，MI-FGSM和Deepfool。我们还提出了一种对抗攻击方法，该方法可增强对抗指纹图像对各种变换（如旋转和翻转）的鲁棒性。我们演示了这些出色的方案，即使没有使用过的模型的内部细节，也可能通过添加微小的扰动将伪造的指纹归类为实时指纹。实验结果从安全性的角度揭示了这些方案的巨大漏洞和威胁，不仅在指纹活跃度检测方面而且在所有深度学习应用中，都迫切需要在反对抗方面给予足够的重视。