Semantic to Structure: Learning Structural Representations for Infringement Detection

TL;DR

This paper introduces a novel method for detecting structural infringement in images, utilizing diffusion models and new datasets, addressing a critical gap in AI-generated content regulation.

Contribution

It defines structural infringement, develops quantitative metrics, creates annotated datasets, and proposes a diffusion model-based detection approach.

Findings

Effective detection of structural infringements achieved

New datasets and metrics facilitate evaluation

Significant improvements over baseline methods

Abstract

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.