Master Face Attacks on Face Recognition Systems
Master Face Attacks on Face Recognition Systems
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DOI：
10.1109/tbiom.2022.3166206
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
H. Nguyen;S. Marcel;J. Yamagishi;I. Echizen
中科院分区：
文献类型：
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作者：
H. Nguyen;S. Marcel;J. Yamagishi;I. Echizen
人脸认证现在被广泛使用，特别是在移动设备上，而不是使用个人识别码或解锁图案进行认证，因为它很方便。因此，它已成为使用演示攻击的攻击者的诱人目标。传统的演示攻击使用受害者的面部图像或视频。先前的工作已经证明了主面孔的存在，即与人脸识别系统中的多个注册模板相匹配的面孔，它们的存在扩展了演示攻击的能力。在本文中，我们报告了对潜变量进化（LVE）的广泛研究，潜变量进化是一种常用于生成主面孔的方法。 LVE 算法在各种场景下运行，并使用多个数据库和/或面部识别系统来识别主面部的属性，并阐明在哪些条件下可以生成强大的主面部。在分析的基础上，我们假设主面孔起源于人脸识别系统嵌入空间的密集区域。最后但并非最不重要的一点是，使用生成的主面孔的模拟演示攻击通常保留了其原始数字形式的错误匹配能力，从而证明主面孔的存在构成了实际威胁。
Face authentication is now widely used, especially on mobile devices, rather than authentication using a personal identification number or an unlock pattern, due to its convenience. It has thus become a tempting target for attackers using a presentation attack. Traditional presentation attacks use facial images or videos of the victim. Previous work has proven the existence of master faces, i.e., faces that match multiple enrolled templates in face recognition systems, and their existence extends the ability of presentation attacks. In this paper, we report an extensive study on latent variable evolution (LVE), a method commonly used to generate master faces. An LVE algorithm was run under various scenarios and with more than one database and/or face recognition system to identify the properties of master faces and to clarify under which conditions strong master faces can be generated. On the basis of analysis, we hypothesize that master faces originate in dense areas in the embedding spaces of face recognition systems. Last but not least, simulated presentation attacks using generated master faces generally preserved the false matching ability of their original digital forms, thus demonstrating that the existence of master faces poses an actual threat.