Similarity Trajectories: Linking Sampling Process to Artifacts in Diffusion-Generated Images

Dennis Menn, Feng Liang, Hung-Yueh Chiang, Diana Marculescu; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 197-206

Artifact detection algorithms are crucial to correcting the output generated by diffusion models. However because of the variety of artifact forms existing methods require substantial annotated data for training. This requirement limits their scalability and efficiency which restricts their wide application. This paper shows that the similarity of denoised images between consecutive time steps during the sampling process is related to the severity of artifacts in images generated by diffusion models. Building on this observation we introduce the concept of Similarity Trajectory to characterize the sampling process and its correlation with the image artifacts presented. Using an annotated data set of 680 images which is only 0.1% of the amount of data used in the prior work we trained a classifier on these trajectories to predict the presence of artifacts in images. By performing 10-fold validation testing on the balanced annotated data set the classifier can achieve an accuracy of 72.35% highlighting the connection between the Similarity Trajectory and the occurrence of artifacts. This approach enables differentiation between artifact-exhibiting and natural-looking images using limited training data.