Towards the Detection of AI-Synthesized Human Face Images
Yuhang Lu Touradj Ebrahimi
Over the past years, image generation and manipulation have achieved
remarkable progress due to the rapid development of generative AI based on deep
learning. Recent studies have devoted significant efforts to address the
problem of face image manipulation caused by deepfake techniques. However, the
problem of detecting purely synthesized face images has been explored to a
lesser extent. In particular, the recent popular Diffusion Models (DMs) have
shown remarkable success in image synthesis. Existing detectors struggle to
generalize between synthesized images created by different generative models.
In this work, a comprehensive benchmark including human face images produced by
Generative Adversarial Networks (GANs) and a variety of DMs has been
established to evaluate both the generalization ability and robustness of
state-of-the-art detectors. Then, the forgery traces introduced by different
generative models have been analyzed in the frequency domain to draw various
insights. The paper further demonstrates that a detector trained with frequency
representation can generalize well to other unseen generative models.