论文标题
gan生成的面孔检测：调查和新观点
GAN-generated Faces Detection: A Survey and New Perspectives
论文作者
论文摘要
生成的对抗网络（GAN）导致产生了非常逼真的面部图像，这些图像已用于虚假的社交媒体帐户和其他可能产生深远影响的虚假信息。因此，相应的GAN-FACE检测技术正在积极开发中，可以检查和暴露此类假面。在这项工作中，我们旨在对GAN-FACE检测的最新进展进行全面审查。我们专注于可以检测到GAN模型生成或合成的面部图像的方法。我们将现有检测作用分为四类：（1）基于深度学习的，（2）基于物理的，（3）基于生理的方法，以及（4）对人类视觉表现的评估和比较。对于每个类别，我们总结了关键想法，并将它们与方法实现联系起来。我们还讨论开放问题并建议未来的研究方向。
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.
