Fantastic Copyrighted Beasts and How (Not) to Generate Them
<|reference_start|>Fantastic Copyrighted Beasts and How (Not) to Generate Them: Recent studies show that image and video generation models can be prompted to reproduce copyrighted content from their training data, raising serious legal concerns around copyright infringement. Copyrighted characters, in particular, pose a difficult challenge for image generation services, with at least one lawsuit already awarding damages based on the generation of these characters. Yet, little research has empirically examined this issue. We conduct a systematic evaluation to fill this gap. First, we build CopyCat, an evaluation suite consisting of diverse copyrighted characters and a novel evaluation pipeline. Our evaluation considers both the detection of similarity to copyrighted characters and generated image's consistency with user input. Our evaluation systematically shows that both image and video generation models can still generate characters even if characters' names are not explicitly mentioned in the prompt, sometimes with only two generic keywords (e.g., prompting with "videogame, plumber" consistently generates Nintendo's Mario character). We then introduce techniques to semi-automatically identify such keywords or descriptions that trigger character generation. Using our evaluation suite, we study runtime mitigation strategies, including both existing methods and new strategies we propose. Our findings reveal that commonly employed strategies, such as prompt rewriting in the DALL-E system, are not sufficient as standalone guardrails. These strategies must be coupled with other approaches, like negative prompting, to effectively reduce the unintended generation of copyrighted characters. Our work provides empirical grounding to the discussion of copyright mitigation strategies and offers actionable insights for model deployers actively implementing them.<|reference_end|>
@article{he2024fantastic,
    title={Fantastic Copyrighted Beasts and How (Not) to Generate Them},
    author={Luxi He, Yangsibo Huang, Weijia Shi, Tinghao Xie, Haotian Liu, Yue
  Wang, Luke Zettlemoyer, Chiyuan Zhang, Danqi Chen, Peter Henderson},
    journal={arXiv preprint arXiv:2406.14526},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14526},
    primaryClass={cs.CV cs.AI cs.CY cs.LG}
}