论文标题
基于GAN的形态是否威胁到面部识别？
Are GAN-based Morphs Threatening Face Recognition?
论文作者
论文摘要
变形攻击是对生物识别系统的威胁，可以改变身份文档中的生物识别参考。这种攻击形式在依赖身份文件（例如边境安全或访问控制）的应用程序中提出了一个重要问题。对面部变形的产生及其检测的研究正在迅速发展，但是很少有具有变形攻击和开源检测工具包的数据集公开。本文通过提供两个数据集和四种形态攻击的相应代码来弥合这一差距：两个依赖于基于OpenCV和Facemorpher的面部标志的两个类型的代码，以及两个使用stylegan 2来生成合成变形的。我们还进行了广泛的实验，以评估四个最先进的面部识别系统的脆弱性，包括FaceNet，VGG-Face，Arcface和ISV。令人惊讶的是，实验表明，尽管在视觉上更具吸引力，但基于Stylegan 2的变形并没有对国家面临识别系统构成重大威胁，因为这些变体是由基于面部标志的简单形态击中的。
Morphing attacks are a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. Research in generation of face morphs and their detection is developing rapidly, however very few datasets with morphing attacks and open-source detection toolkits are publicly available. This paper bridges this gap by providing two datasets and the corresponding code for four types of morphing attacks: two that rely on facial landmarks based on OpenCV and FaceMorpher, and two that use StyleGAN 2 to generate synthetic morphs. We also conduct extensive experiments to assess the vulnerability of four state-of-the-art face recognition systems, including FaceNet, VGG-Face, ArcFace, and ISV. Surprisingly, the experiments demonstrate that, although visually more appealing, morphs based on StyleGAN 2 do not pose a significant threat to the state to face recognition systems, as these morphs were outmatched by the simple morphs that are based facial landmarks.
