Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Risk

arXiv:2403.09450v1 Announce Type: new
Abstract: While diffusion models have recently demonstrated remarkable progress in generating realistic images, privacy risks also arise: published models or APIs could generate training images and thus leak privacy-sensitive training information. In this paper, we reveal a new risk, Shake-to-Leak (S2L), that fine-tuning the pre-trained models with manipulated data can amplify the existing privacy risks. We demonstrate that S2L could occur in various standard fine-tuning strategies for diffusion models, including concept-injection methods (DreamBooth and Textual Inversion) …

amplify arxiv cs.lg diffusion diffusion models fine-tuning generative leak privacy risk type