MCID: Multi-Aspect Copyright Infringement Detection for Generated Images

Abstract

With the rapid advancement of generative models, we can now create highly realistic images. This represents a significant technical breakthrough but also introduces new challenges for copyright protection. Previous methods for detecting copyright infringement in AI-generated images mainly depend on global similarity. However, real-world infringement often occurs only on certain attributes rather than being a global infringement. To address these challenges, we propose a novel Multi-aspect Copyright Infringement Detection (MCID) task, which encompasses various types of infringement, including content, style, structure, and intellectual property infringement. We further develop the Hybrid Infringement Detection Model (HIDM) to address the MCID task. By combining feature-based methods with VLMs, it enables the detection of various infringement types and provides interpretable results. To ensure the MCID task meets actual legal requirements, we construct a Large-Scale Copyright Dataset (LSCD) with clear author copyright ownership. Based on LSCD, we provide a benchmark annotated by legal experts for performance evaluation. Experimental results show that HIDM effectively detects various types of image copyright infringement and offers a more interpretable and superior solution compared to previous methods.