  Diffusion models (DMs) are generative models that learn to synthesize images
from Gaussian noise. DMs can be trained to do a variety of tasks such as image
generation and image super-resolution. Researchers have made significant
improvement in the capability of synthesizing photorealistic images in the past
few years. These successes also hasten the need to address the potential misuse
of synthesized images. In this paper, we highlight the effectiveness of
computing local statistics, as opposed to global statistics, in distinguishing
digital camera images from DM-generated images. We hypothesized that local
statistics should be used to address the spatial non-stationarity problem in
images. We show that our approach produced promising results and it is also
robust to various perturbations such as image resizing and JPEG compression.

Computer Vision and Pattern Recognition