Summary of Revealing the Unseen: Guiding Personalized Diffusion Models to Expose Training Data, by Xiaoyu Wu et al.
Revealing the Unseen: Guiding Personalized Diffusion Models to Expose Training Data
by Xiaoyu Wu, Jiaru Zhang, Steven Wu
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 explores the potential risks of data leakage and copyright infringement when using diffusion models (DMs) for few-shot fine-tuning. Specifically, it investigates whether training data can be extracted from shared online checkpoints. To address this question, the authors propose FineXtract, a framework that approximates fine-tuning as a gradual shift in the model’s learned distribution. The method extrapolates the models before and after fine-tuning to guide generation toward high-probability regions within the fine-tuned data distribution. A clustering algorithm is then applied to extract the most probable images from those generated using this extrapolated guidance. Experiments on DMs fine-tuned with datasets such as WikiArt, DreamBooth, and real-world checkpoints posted online validate the effectiveness of FineXtract, extracting approximately 20% of fine-tuning data in most cases, significantly surpassing baseline performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to find out what pictures were used to train an AI model. When people share their custom-made AI models online, they might not realize that others can use those models to figure out which specific pictures were used to train it. This could be a problem because some of those pictures might belong to someone else and the AI model is using them without permission. The researchers developed a new method called FineXtract to find this information. They tested it with several different datasets and found that they can extract around 20% of the training data from online models, which is much better than previous methods. |
Keywords
» Artificial intelligence » Clustering » Diffusion » Few shot » Fine tuning » Probability
