Self-supervised scheme for generalizing GAN image detection

Yonghyun Jeong, Doyeon Kim, Pyounggeon Kim, Youngmin Ro, Jongwon Choi

Abstract: Highlights•A novel framework to train a GAN detector in the self-supervision scheme.•New architecture employing multiple autoencoders to reproduce the fingerprints of GANs.•Outstanding robustness to unknown GANs compared to the supervised GAN detectors.•Impressive performance for zero-shot and few-shot transfer learning.•Detailed analysis of the proposed framework with numerous ablation tests.