Generative Adversarial Networks (GAN) have led to the generation of very realistic face images, which have been used in fake social media accounts and other disinformation matters that can generate profound impacts. Therefore, the corresponding GAN-face detection techniques are under active development that can examine and expose such fake faces. In this work, we aim to provide a comprehensive review of recent progress in GAN-face detection. We focus on methods that can detect face images that are generated or synthesized from GAN models. We classify the existing detection works into four categories: (1) deep learning-based, (2) physical-based, (3) physiological-based methods, and (4) evaluation and comparison against human visual performance. For each category, we summarize the key ideas and connect them with method implementations. We also discuss open problems and suggest future research directions.

翻译：生成对抗网络（GAN）已能够生成极其逼真的人脸图像，这些图像被用于虚假社交媒体账号及其他可产生深远影响的虚假信息事件中。因此，相应的GAN人脸检测技术正得到积极发展，用以查验和揭露此类虚假人脸。本文旨在全面回顾GAN人脸检测领域的最新进展，重点关注能够检测由GAN模型生成或合成人脸图像的方法。我们将现有检测研究划分为四类：(1) 基于深度学习的方法、(2) 基于物理特性的方法、(3) 基于生理特征的方法，以及(4) 与人类视觉性能的评估比较。针对每种类别，我们总结核心思想，并将其与方法实现相关联。同时，我们探讨了当前存在的开放性问题，并提出了未来研究方向建议。