论文标题
GAN可以同样将面部识别系统作为基于里程碑的形态威胁到面部识别系统吗？ - 脆弱性和检测
Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detection
论文作者
论文摘要
面部变形的主要目的是结合不同数据主体（例如恶意演员和同伙）的面部图像，以生成一个可以同样验证的面部图像，这两个贡献数据主体。在本文中，我们提出了一个新的框架，用于使用较新的生成对抗网络（GAN） - Stylegan产生面部变形。与较早的作品相反，我们产生了高质量和高分辨率的现实形态，为1024 $ \ times $ 1024像素。凭借2500个变形的面部图像的新创建的变形数据集，我们在这项工作中提出了一个关键的问题。 \ textIt {（i）gan可以同样地将面部识别系统（FRS）与基于里程碑的形态同样威胁？}寻求答案，我们基准了商业上杂货店的FRS（COTS）（COTS）和深度学习的FRS（Arcface）的脆弱性。这项工作还基准了使用已建立的变形攻击检测（MAD）方案对基于里程碑的形态产生对基于里程碑的形态的检测方法。
The primary objective of face morphing is to combine face images of different data subjects (e.g. a malicious actor and an accomplice) to generate a face image that can be equally verified for both contributing data subjects. In this paper, we propose a new framework for generating face morphs using a newer Generative Adversarial Network (GAN) - StyleGAN. In contrast to earlier works, we generate realistic morphs of both high-quality and high resolution of 1024$\times$1024 pixels. With the newly created morphing dataset of 2500 morphed face images, we pose a critical question in this work. \textit{(i) Can GAN generated morphs threaten Face Recognition Systems (FRS) equally as Landmark based morphs?} Seeking an answer, we benchmark the vulnerability of a Commercial-Off-The-Shelf FRS (COTS) and a deep learning-based FRS (ArcFace). This work also benchmarks the detection approaches for both GAN generated morphs against the landmark based morphs using established Morphing Attack Detection (MAD) schemes.
