通过原创性估计与泛化处理解决AI图像生成中的版权问题
Tackling Copyright Issues in AI Image Generation Through Originality Estimation and Genericization
AI总结：
提出一种泛化处理方法，通过原创性度量指标修改生成模型输出以降低模仿受版权材料特征的风险，并实现PREGen方法，将生成受版权角色的可能性降低超一半。
摘要：
生成式AI技术的快速发展引发了显著的版权担忧，导致针对AI开发者的诉讼数量众多。值得注意的是，生成式AI生成受版权保护角色图像的能力已在文献中得到充分记录，尽管已有多种缓解版权问题的技术被研究，但重大风险仍然存在。在此，我们提出一种泛化处理方法，该方法修改生成模型的输出，使其更加通用，并降低模仿受版权保护材料独特特征的可能性。为实现这一目标，我们引入了一个用于量化数据原创性水平的度量指标，该指标通过从生成模型中抽取样本来估计，并应用于泛化处理过程。作为实际实现，我们提出了PREGen（提示重写增强泛化处理），该方法将我们的泛化处理技术与现有缓解技术相结合。与现有方法相比，当使用受版权保护角色的名称作为提示时，PREGen将生成受版权保护角色的可能性降低了超过一半。此外，尽管生成模型即使在提示中未直接提及角色名称时也能生成受版权保护角色，但PREGen在这些情况下几乎完全阻止了此类角色的生成。最终，本研究推进了量化和加强版权保护的计算方法，从而提供了促进负责任生成式AI发展的实用方法论。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
