Summary of Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Risk, by Zhangheng Li et al.
Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Risk
by Zhangheng Li, Junyuan Hong, Bo Li, Zhangyang Wang
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper reveals a new privacy risk, Shake-to-Leak (S2L), that fine-tuning pre-trained diffusion models with manipulated data can amplify existing privacy risks. Researchers demonstrate that S2L can occur in various fine-tuning strategies for diffusion models, including concept-injection and parameter-efficient methods. The study shows that S2L can increase extracted private samples from almost 0 to an average of 15.8 per target domain, amplifying the state-of-the-art membership inference attack (MIA) by 5.4% AUC. This discovery highlights the severity of privacy risks associated with diffusion models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new problem that can happen when we use special AI models to generate images or text. These models are really good at making things look realistic, but they can also leak private information. The researchers found out that if we make small changes to the data used to train these models, it can make them even worse at keeping secrets. This is a problem because it means that people’s personal information could be stolen or used in ways they don’t want. The study shows that this can happen with many different kinds of AI models and methods. |
Keywords
* Artificial intelligence * Auc * Fine tuning * Inference * Parameter efficient
