From Imitation to Innovation: The Emergence of AI Unique Artistic Styles and the Challenge of Copyright Protection

2025-07-07

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.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: AI for Accessibility
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Algorithmic accountability and transparency on the web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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

• 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

• ArtBulb framework for AI art copyright judgment
• Style description-based multimodal clustering method
• First benchmark dataset AICD for AI art