论文标题
从地标和生成对抗网络的面部变形攻击的脆弱性分析
Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks
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。面对面变形攻击检测的研究正在迅速发展，但是很少有具有多种攻击形式的数据集公开可用。本文根据OPENCV，FACEMORPHER，WEBMORPH和一个生成的对抗网络（StyleGAN），通过提供四种不同类型的变形攻击来弥合这一差距，该数据集由来自三个公共面部数据集的原始面部图像生成。我们还进行了广泛的实验，以评估最先进的面部识别系统的脆弱性，尤其是面部，vgg-face和Arcface。该实验表明，与面部相比，VGG-FACE虽然面部识别系统较差，但也不太容易受到变形攻击的影响。另外，我们观察到，风格产生的天真形态不会构成重大威胁。
Morphing attacks is 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 face morphing attack detection is developing rapidly, however very few datasets with several forms of attacks are publicly available. This paper bridges this gap by providing a new dataset with four different types of morphing attacks, based on OpenCV, FaceMorpher, WebMorph and a generative adversarial network (StyleGAN), generated with original face images from three public face datasets. We also conduct extensive experiments to assess the vulnerability of the state-of-the-art face recognition systems, notably FaceNet, VGG-Face, and ArcFace. The experiments demonstrate that VGG-Face, while being less accurate face recognition system compared to FaceNet, is also less vulnerable to morphing attacks. Also, we observed that naïve morphs generated with a StyleGAN do not pose a significant threat.
