GAN-generated Faces Detection: A Survey and New Perspectives
It addresses the problem of detecting fake faces for security and media integrity, but is incremental as it is a survey paper.
This survey reviews recent progress in detecting GAN-generated face images, which are used in fake social media accounts and disinformation, by categorizing existing detection methods into four types and discussing open problems and future directions.
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.