Tackling Copyright Issues in AI Image Generation Through Originality Estimation and Genericization

• URL: http://arxiv.org/abs/2406.03341v7
• Date: Mon, 31 Mar 2025 02:53:45 GMT
• Title: Tackling Copyright Issues in AI Image Generation Through Originality Estimation and Genericization
• Authors: Hiroaki Chiba-Okabe, Weijie J. Su,
• Abstract summary: We propose a genericization method that modifies the outputs of a generative model to make them more generic and less likely to imitate copyrighted materials.<n>As a practical implementation, we introduce PREGen, which combines our genericization method with an existing mitigation technique.
• Score: 25.703494724823756
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid progress of generative AI technology has sparked significant copyright concerns, leading to numerous lawsuits filed against AI developers. Notably, generative AI's capacity for generating images of copyrighted characters has been well documented in the literature, and 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 imitate distinctive features of copyrighted materials. To achieve this, we introduce a metric for quantifying the level of originality of data, estimated by drawing samples from a generative model, and applied in the genericization process. As a practical implementation, we introduce PREGen (Prompt Rewriting-Enhanced Genericization), which combines our genericization method with an existing mitigation technique. 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. 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. Ultimately, this study advances computational approaches for quantifying and strengthening copyright protection, thereby providing practical methodologies to promote responsible generative AI development.

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