Abstract
Face anti-spoofing is designed to keep face recognition systems from recognizing fake faces as the genuine users. While advanced face anti-spoofing methods are developed, new types of spoof attacks are also being created and becoming a threat to all existing systems. We define the detection of unknown spoof attacks as Zero-Shot Face Anti-spoofing (ZSFA). Previous works of ZSFA only study 1-2 types of spoof attacks, such as print/replay attacks, which limits the insight of this problem. In this work, we expand the ZSFA problem to a wide range of 13 types of spoof attacks, including print attack, replay attack, 3D mask attacks, and so on. A novel Deep Tree Network (DTN) is proposed to tackle the ZSFA. The tree is learned to partition the spoof samples into semantic sub-groups in an unsupervised fashion. When a data sample arrives, being know or unknown attacks, DTN routes it to the most similar spoof cluster, and make the binary decision. In addition, to enable the study of ZSFA, we introduce the first face anti-spoofing database that contains diverse types of spoof attacks. Experiments show that our proposed method achieves the state of the art on multiple testing protocols of ZSFA.
Abstract (translated)
人脸反欺骗是为了防止人脸识别系统将假脸识别为真正的用户。在开发先进的人脸反欺骗方法的同时，新类型的欺骗攻击也在产生，并成为对所有现有系统的威胁。我们将未知欺骗攻击的检测定义为零触发面反欺骗（zsfa）。zsfa以前的工作只研究了1-2种类型的欺骗攻击，例如打印/重放攻击，这限制了对该问题的深入了解。在这项工作中，我们将zsfa问题扩展到13种类型的欺骗攻击，包括打印攻击、重放攻击、3D遮罩攻击等。提出了一种新的深树网络（DTN）来解决zsfa问题。树被学习以无监督的方式将欺骗样本划分为语义子组。当一个已知或未知的数据样本到达时，DTN将其路由到最相似的欺骗集群，并做出二进制决策。此外，为了支持对zsfa的研究，我们引入了第一个包含各种类型欺骗攻击的人脸反欺骗数据库。实验表明，本文提出的方法在zsfa多测试协议上达到了最先进的水平。
URL
https://arxiv.org/abs/1904.02860
