从模仿到创新：AI独特艺术风格的出现与版权保护挑战
From Imitation to Innovation: The Emergence of AI Unique Artistic Styles and the Challenge of Copyright Protection
AI总结：
本文提出可解释的AI艺术版权判定框架ArtBulb，结合风格描述多模态聚类与多模态大语言模型，并发布首个专家标注基准AICD，以应对AI艺术风格独创性评估与版权保护挑战。
摘要：
当前法律框架认为，当AI生成作品满足独创性要求并包含实质性人类智力投入时，即有资格获得版权保护。然而，针对AI艺术版权的系统性法律标准和可靠评估方法仍然缺乏。通过对法律先例的全面分析，我们确立了判定独特艺术风格的三项基本标准：风格一致性、创作独特性和表达准确性。为应对这些挑战，我们提出了ArtBulb，一个可解释且可量化的AI艺术版权判定框架，该框架将一种新颖的基于风格描述的多模态聚类方法与多模态大语言模型（MLLMs）相结合。我们还推出了AICD，这是首个由艺术家和法律专家标注的AI艺术版权基准数据集。实验结果表明，ArtBulb在定量和定性评估中均优于现有模型。我们的工作旨在弥合法律界与技术界之间的鸿沟，并引起对AI艺术版权这一社会问题的更多关注。
英文摘要：
Current legal frameworks consider AI-generated works eligible for copyright protection when they meet originality requirements and involve substantial human intellectual input. However, systematic legal standards and reliable evaluation methods for AI art copyrights are lacking. Through comprehensive analysis of legal precedents, we establish three essential criteria for determining distinctive artistic style: stylistic consistency, creative uniqueness, and expressive accuracy. To address these challenges, we introduce ArtBulb, an interpretable and quantifiable framework for AI art copyright judgment that combines a novel style description-based multimodal clustering method with multimodal large language models (MLLMs). We also present AICD, the first benchmark dataset for AI art copyright annotated by artists and legal experts. Experimental results demonstrate that ArtBulb outperforms existing models in both quantitative and qualitative evaluations. Our work aims to bridge the gap between the legal and technological communities and bring greater attention to the societal issue of AI art copyrights.
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。
