论文标题
基于深脸代表的化妆演示攻击的检测
Detection of Makeup Presentation Attacks based on Deep Face Representations
论文作者
论文摘要
面部化妆品具有实质性改变面部外观的能力，这可能会对面部识别的决策产生负面影响。此外，最近表明，可以滥用化妆品的应用来发起所谓的化妆演示攻击。在此类攻击中，攻击者可能会采用重型化妆，以实现目标对象的面部外观。在这项工作中，我们评估了COTS面部识别系统的脆弱性，该系统采用公开化妆引起的面部欺骗（MIFS）数据库的构成表现攻击。结果表明，化妆表现攻击可能会严重影响面部识别系统的安全性。此外，我们提出了一种攻击检测方案，该方案通过分析从潜在的化妆表现攻击和相应的目标面部图像中获得的深面表示中的差异来区分化妆表现攻击与真正的身份验证尝试。拟议的检测系统采用基于机器学习的分类器，该分类器经过合成生成的化妆呈现攻击，利用生成的对手网络与图像扭曲结合使用了面部化妆转移。使用MIFS数据库进行的实验评估揭示了将真正的身份验证尝试与化妆表现攻击分开的任务的检测等于0.7％。
Facial cosmetics have the ability to substantially alter the facial appearance, which can negatively affect the decisions of a face recognition. In addition, it was recently shown that the application of makeup can be abused to launch so-called makeup presentation attacks. In such attacks, the attacker might apply heavy makeup in order to achieve the facial appearance of a target subject for the purpose of impersonation. In this work, we assess the vulnerability of a COTS face recognition system to makeup presentation attacks employing the publicly available Makeup Induced Face Spoofing (MIFS) database. It is shown that makeup presentation attacks might seriously impact the security of the face recognition system. Further, we propose an attack detection scheme which distinguishes makeup presentation attacks from genuine authentication attempts by analysing differences in deep face representations obtained from potential makeup presentation attacks and corresponding target face images. The proposed detection system employs a machine learning-based classifier, which is trained with synthetically generated makeup presentation attacks utilizing a generative adversarial network for facial makeup transfer in conjunction with image warping. Experimental evaluations conducted using the MIFS database reveal a detection equal error rate of 0.7% for the task of separating genuine authentication attempts from makeup presentation attacks.
