Towards Universal GAN Image Detection
This addresses the need for universal forensic tools to detect fake images in real-world scenarios, though it is an incremental step.
The paper tackles the problem of limited robustness and generalization in GAN image detectors by proposing a new method based on limited sub-sampling and contrastive learning, achieving good robustness to image impairments and generalization to unseen architectures.
The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools. Many GAN image detectors have been proposed, recently. In real world scenarios, however, most of them show limited robustness and generalization ability. Moreover, they often rely on side information not available at test time, that is, they are not universal. We investigate these problems and propose a new GAN image detector based on a limited sub-sampling architecture and a suitable contrastive learning paradigm. Experiments carried out in challenging conditions prove the proposed method to be a first step towards universal GAN image detection, ensuring also good robustness to common image impairments, and good generalization to unseen architectures.