提出检测图像结构抄袭的新方法，解决AI生成内容侵权难题。
Semantic to Structure: Learning Structural Representations for Infringement Detection
- 用扩散模型与LLM合成数据，构建结构侵权检测新数据集
- 在真实与合成数据上均实现显著检测效果
- 适合版权保护、AI内容审核领域研究者使用
图像的结构信息对美学评估至关重要，艺术界普遍认为模仿他人作品的结构构成创作者权利侵犯。扩散模型的发展使得AI生成内容能复刻艺术家的结构特征，但有效的检测手段仍缺失。本文将此现象定义为“结构侵权”，并提出相应的检测方法。同时，我们设计了量化指标，并构建了两个手动标注的数据集：用于合成数据的SIA数据集和用于真实数据的SIR数据集。由于当前缺乏结构侵权检测数据集，我们提出一种基于扩散模型与大语言模型的新数据合成策略，成功训练出结构侵权检测模型。实验结果表明，该方法能在标注测试集上有效识别结构侵权，取得显著性能提升。
原文摘要 · Abstract (English)
Structural information in images is crucial for aesthetic assessment, and it is widely recognized in the artistic field that imitating the structure of other works significantly infringes on creators' rights. The advancement of diffusion models has led to AI-generated content imitating artists' structural creations, yet effective detection methods are still lacking. In this paper, we define this phenomenon as "structural infringement" and propose a corresponding detection method. Additionally, we develop quantitative metrics and create manually annotated datasets for evaluation: the SIA dataset of synthesized data, and the SIR dataset of real data. Due to the current lack of datasets for structural infringement detection, we propose a new data synthesis strategy based on diffusion models and LLM, successfully training a structural infringement detection model. Experimental results show that our method can successfully detect structural infringements and achieve notable improvements on annotated test sets.
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