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.

翻译：人脸变形攻击日益成为生物识别研究人员关注的焦点，因其可用于欺骗人脸识别系统。此类攻击可在图像层面（有监督）或表征层面（无监督）生成。以往的无监督变形攻击依赖于生成对抗网络。近期，研究人员采用StyleGAN编码图像的线性插值来生成变形攻击。本文提出一种利用StyleGAN解耦生成高质量变形攻击的新方法。该方法名为MLSD-GAN，通过对解耦后的潜在变量进行球面插值，生成逼真且多样化的变形攻击。我们基于两种深度学习人脸识别技术评估了MLSD-GAN的威胁性。结果表明，MLSD-GAN对人脸识别系统构成显著威胁，因其能生成高度有效欺骗这些系统的变形攻击。