Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative
  Privacy Risk
Zhangheng Li Junyuan Hong Bo Li Zhangyang Wang
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) and parameter-efficient methods
(LoRA and Hypernetwork), as well as their combinations. In the worst case, S2L
can amplify the state-of-the-art membership inference attack (MIA) on diffusion
models by $5.4\%$ (absolute difference) AUC and can increase extracted private
samples from almost $0$ samples to $16.3$ samples on average per target domain.
This discovery underscores that the privacy risk with diffusion models is even
more severe than previously recognized. Codes are available at
https://github.com/VITA-Group/Shake-to-Leak.