Summary of Fantastic Copyrighted Beasts and How (not) to Generate Them, by Luxi He et al.
Fantastic Copyrighted Beasts and How (Not) to Generate Them
by Luxi He, Yangsibo Huang, Weijia Shi, Tinghao Xie, Haotian Liu, Yue Wang, Luke Zettlemoyer, Chiyuan Zhang, Danqi Chen, Peter Henderson
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); 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 The paper investigates the issue of image and video generation models reproducing copyrighted content from their training data, specifically focusing on copyrighted characters. The authors build an evaluation suite, CopyCat, consisting of diverse copyrighted characters, to assess both the detection of similarity to these characters and the generated image’s consistency with user input. The study finds that both image and video generation models can still generate characters even if character names are not explicitly mentioned in the prompt, using only generic keywords. To mitigate this issue, the authors propose techniques for semi-automatically identifying keywords or descriptions that trigger character generation and introduce runtime mitigation strategies. The findings suggest that existing methods, such as prompt rewriting, are insufficient on their own and must be combined with other approaches, like negative prompting, to effectively reduce the unintended generation of copyrighted characters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how image and video generation models can accidentally create copies of copyrighted characters. This is a problem because it means companies might need to pay damages for creating these characters without permission. The authors created a system to test how well different models do at recognizing when they’re generating copyrighted characters. They found that some models are very good at generating characters, even if the character’s name isn’t mentioned in the prompt. To fix this problem, the authors suggest ways to identify when a model is about to generate a copyrighted character and propose strategies for reducing these instances. |
Keywords
» Artificial intelligence » Prompt » Prompting
