"Paper: Stable Diffusion 'Memorizes' Some Images, Sparking Privacy Concerns"

Artificial Intelligence (AI) researchers from Google, DeepMind, UC Berkeley, Princeton, and ETH Zurich have published a paper describing an adversarial attack that can extract a small percentage of training images from latent diffusion AI image synthesis models such as Stable Diffusion. This attack challenges the notion that image synthesis models do not memorize their training data and that, if not published, training data may remain private. AI image synthesis models have sparked ethical debate and legal action. Proponents and opponents of these new technologies often debate the privacy and copyright issues of generative AI tools. Further igniting either side of the debate might have a significant impact on potential legal regulation of the technology. Therefore, this latest work has piqued the interest of AI researchers. This article continues to discuss the study on extracting training data from diffusion models that is raising privacy concerns. 

Ars Technica reports "Paper: Stable Diffusion 'Memorizes' Some Images, Sparking Privacy Concerns"