Summary of Disguised Copyright Infringement Of Latent Diffusion Models, by Yiwei Lu et al.
Disguised Copyright Infringement of Latent Diffusion Models
by Yiwei Lu, Matthew Y.R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu
First submitted to arxiv on: 10 Apr 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 The paper addresses a critical issue in generative models: copyright infringement. When these models produce samples similar to copyrighted data, it’s often due to indirect access rather than direct inclusion of the original material. The authors argue that traditional auditing methods overlook this type of infringement, where disguised samples are created to evade detection. They propose a novel algorithm for generating such disguises and introduce a broader notion of acknowledgment for understanding indirect access. To combat this issue, they provide a method for detecting disguised copyright infringement, enhancing the existing toolbox. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative models can accidentally copy copyrighted data without directly using it. This is called disguised copyright infringement, where the copied information looks very different from the original but still gets learned by the model. The authors of this paper want to understand how this happens and how to stop it. They came up with a way to create these disguises and figured out how to detect them. They’re sharing their code so others can use it too. |
