Abstract
Face-morphing attacks are a growing concern for biometric researchers, as they can be used to fool face recognition systems (FRS). These attacks can be generated at the image level (supervised) or representation level (unsupervised). Previous unsupervised morphing attacks have relied on generative adversarial networks (GANs). More recently, researchers have used linear interpolation of StyleGAN-encoded images to generate morphing attacks. In this paper, we propose a new method for generating high-quality morphing attacks using StyleGAN disentanglement. Our approach, called MLSD-GAN, spherically interpolates the disentangled latents to produce realistic and diverse morphing attacks. We evaluate the vulnerability of MLSD-GAN on two deep-learning-based FRS techniques. The results show that MLSD-GAN poses a significant threat to FRS, as it can generate morphing attacks that are highly effective at fooling these systems.
Abstract (translated)
面部变形攻击是生物特征研究者越来越关注的问题，因为这些攻击可以使用户欺骗人脸识别系统（FRS）。这些攻击可以在图像级别（监督）或表示级别（无监督）进行生成。之前无监督的变形攻击依赖于生成对抗网络（GANs）。更最近，研究人员使用StyleGAN编码的图像的线性插值生成变形攻击。在本文中，我们提出了一种利用StyleGAN解离生成新方法来生成高质量变形攻击。我们的方法称为MLSD-GAN，它使用球形插值来解离变差的 latent，产生真实和多样化的变形攻击。我们评估了MLSD-GAN在两种基于深度学习的FRS技术上的安全性。结果显示，MLSD-GAN对FRS构成了显著威胁，因为它可以生成欺骗性效果很强的变形攻击。
URL
https://arxiv.org/abs/2404.12679
