Abstract

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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 15.8 samples on average per target domain. This discovery underscores that the privacy risk with diffusion models is even more severe than previously recognized.

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Author

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Zhangheng Li, Junyuan Hong, Bo Li, Zhangyang Wang. (IEEE SatML 2024)