MLSD-GAN：利用潜在语义解缠生成强健的高质量人脸变形攻击
MLSD-GAN -- Generating Strong High Quality Face Morphing Attacks using Latent Semantic Disentanglement
AI总结：
提出MLSD-GAN方法，利用StyleGAN潜在语义解缠和球形插值生成高质量人脸变形攻击，并在两种深度学习人脸识别系统上验证其显著威胁性。
摘要：
人脸变形攻击日益引起生物识别研究者的关注，因为它们可被用来欺骗人脸识别系统（FRS）。这些攻击可以在图像层面（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（GAN）。最近，研究者使用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解缠生成高质量变形攻击的新方法。我们的方法称为MLSD-GAN，它通过球形插值解缠后的潜在向量来生成逼真且多样的变形攻击。我们在两种基于深度学习的人脸识别技术上评估了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，因为它能生成高度有效欺骗这些系统的变形攻击。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
