  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 are convinced that this work provides the foundation and starting point for
further research on effective detection of DM-generated images.

Computer Vision and Pattern Recognition