Towards the Detection of Diffusion Model Deepfakes

Diffusion models (DMs) have recently emerged as a promising method in image synthesis. However, to date, only little attention has been paid to the detection of DM-generated images, which is critical to prevent adverse impacts on our society. In this work, we address this pressing challenge from two different angles: First, we evaluate the performance of state-of-the-art detectors, which are very effective against images generated by generative adversarial networks (GANs), on a variety of DMs. Second, we analyze DM-generated images in the frequency domain and study different factors that influence the spectral properties of these images. Most importantly, we demonstrate that GANs and DMs produce images with different characteristics, which requires adaptation of existing classifiers to ensure reliable detection. We believe this work provides the foundation and starting point for further research to detect DM deepfakes effectively.