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：生成的Aversarial Networks(GAN)生成了非常现实的面部图像,这些图像被用于虚假的社交媒体账户和可能产生深远影响的其他虚假信息事项,因此,正在积极开发相应的GAN脸部探测技术,以检查和揭露这些假面孔。在这项工作中,我们的目标是全面审查GAN脸部探测工作的最新进展。我们侧重于能够检测从GAN模型生成或合成的面部图像的方法。我们把现有的探测工作分为四类:(1) 深层学习、(2) 物理、(3) 生理方法,(4) 与人类视觉表现相比,评估和比较。我们总结了关键思想,并将它们与方法的实施联系起来。我们还讨论公开的问题,并提出未来的研究方向。