提出AI艺术风格版权判定框架，解决原创性评估难题
From Imitation to Innovation: The Emergence of AI Unique Artistic Styles and the Challenge of Copyright Protection
- 构建基于风格描述的多模态聚类与大模型结合的可解释评估方法
- 在首个由艺术家和专家标注的AICD数据集上表现优于现有模型
- 为法律界提供可量化、可验证的艺术风格判别标准，适合政策制定者参考
当前法律认为，只要满足原创性要求并有实质性人类智力投入，AI生成作品可获版权保护。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缺失。通过分析法律判例，我们确立了判断独特艺术风格的三个核心标准：风格一致性、创作独特性与表达准确性。为此，我们提出ArtBulb框架，结合创新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），实现可解释、可量化的版权判断。同时发布AICD数据集——首个由艺术家与法律专家共同标注的AI艺术版权数据集。实验表明，ArtBulb在定量与定性评估中均优于现有模型。本研究旨在弥合法律与技术之间的鸿沟，推动社会对AI艺术版权问题的关注。
原文摘要 · Abstract (English)
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.
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