MIT researchers find AI-generated images grow harder to trace back to training data as models scale

Attribution decay is the phenomenon where a single training image's influence on a diffusion model's outputs shrinks as the training dataset grows larger. MIT CSAIL researchers Zheng Dai and David Gifford found this follows an inverse power law across 24 diffusion ensembles trained on datasets ranging from 256 to over 160,000 images, meaning removing one image, or even an artist's entire body of work, barely changes outputs at scale. Follow daily.dev for research shaping how AI copyright disputes over training data get argued.

They built a diffusion ensemble architecture that lets researchers exactly remove the influence of specific training images without retraining the entire model each time. This method made it feasible to test 24 different diffusion ensembles across datasets of varying sizes, from 256 images to over 160,000, and measure how single-image influence changes with scale. daily.dev surfaces techniques like this for developers tracking how generative models are evaluated.