Tackling GenAI Copyright Issues: Originality Estimation and Genericization
<|reference_start|>Tackling GenAI Copyright Issues: Originality Estimation and Genericization: The rapid progress of generative AI technology has sparked significant copyright concerns, leading to numerous lawsuits filed against AI developers. While various techniques for mitigating copyright issues have been studied, significant risks remain. Here, we propose a genericization method that modifies the outputs of a generative model to make them more generic and less likely to infringe copyright. To achieve this, we introduce a metric for quantifying the level of originality of data in a manner that is consistent with the legal framework. This metric can be estimated by drawing samples from a generative model, which is then used for the genericization process. As a practical implementation, we introduce PREGen, which combines our genericization method with an existing mitigation technique. Experiments demonstrate that our genericization method successfully modifies the output of a text-to-image generative model so that it produces more generic, copyright-compliant images. Compared to the existing method, PREGen reduces the likelihood of generating copyrighted characters by more than half when the names of copyrighted characters are used as the prompt, dramatically improving the performance. Additionally, while generative models can produce copyrighted characters even when their names are not directly mentioned in the prompt, PREGen almost entirely prevents the generation of such characters in these cases.<|reference_end|>
@article{chiba-okabe2024tackling,
    title={Tackling GenAI Copyright Issues: Originality Estimation and
  Genericization},
    author={Hiroaki Chiba-Okabe and Weijie J. Su},
    journal={arXiv preprint arXiv:2406.03341},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03341},
    primaryClass={cs.LG cs.AI stat.AP stat.ME stat.ML}
}