From Imitation to Innovation: The Emergence of AI Unique Artistic Styles and the Challenge of Copyright Protection
🤖 AI Summary
Current copyright attribution for AI-generated art lacks systematic legal standards and quantifiable evaluation methodologies. To address this, we propose ArtBulb—the first explainable and quantifiable framework for AI art copyright assessment—grounded in three core dimensions: stylistic consistency, creative distinctiveness, and expressive fidelity. We introduce AICD, the first benchmark dataset for AI art copyright, collaboratively annotated by professional artists and legal experts. Methodologically, ArtBulb innovatively integrates style description–driven multimodal clustering with multimodal large language models (MLLMs) to enable automated, fine-grained artistic style identification and analysis. Extensive experiments demonstrate that ArtBulb significantly outperforms existing approaches in both quantitative metrics and qualitative interpretability, substantially enhancing accuracy, transparency, and interdisciplinary credibility in copyright determination.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Lack of legal standards for AI art copyright evaluation
Need for criteria to determine AI artistic style originality
Absence of reliable frameworks for AI copyright judgment
Innovation
Methods, ideas, or system contributions that make the work stand out.
ArtBulb framework for AI art copyright judgment
Style description-based multimodal clustering method
First benchmark dataset AICD for AI art
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