Towards the Detection of AI-Synthesized Human Face Images
This addresses the detection of synthetic face images, which is crucial for security and media integrity, but is incremental as it builds on existing detection methods with a new benchmark and frequency analysis.
The paper tackled the problem of detecting AI-synthesized human face images, establishing a benchmark with GANs and Diffusion Models and showing that a detector trained on frequency representations generalizes well to unseen generative models.
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.