# copyright Plug-in Authorization for Human Content Copyright Protection in Text-to-Image Model

arXiv: https://arxiv.org/abs/2404.11962 · HTML: https://mlsanity.com/paper/2404.11962/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2024.

## Built on by (most cited first, 2 of 2)

- [Automatic Jailbreaking of the Text-to-Image Generative AI Systems](https://mlsanity.com/paper/2405.16567/) (2024)
- [Replication in Visual Diffusion Models: A Survey and Outlook](https://mlsanity.com/paper/2408.00001/) (2024)

## Abstract (arXiv)

This paper addresses the contentious issue of copyright infringement in images generated by text-to-image models, sparking debates among AI developers, content creators, and legal entities. State-of-the-art models create high-quality content without crediting original creators, causing concern in the artistic community. To mitigate this, we propose the copyright Plug-in Authorization framework, introducing three operations: addition, extraction, and combination. Addition involves training a copyright plug-in for specific copyright, facilitating proper credit attribution. Extraction allows creators to reclaim copyright from infringing models, and combination enables users to merge different copyright plug-ins. These operations act as permits, incentivizing fair use and providing flexibility in authorization. We present innovative approaches,"Reverse LoRA" for extraction and "EasyMerge" for seamless combination. Experiments in artist-style replication and cartoon IP recreation demonstrate copyright plug-ins' effectiveness, offering a valuable solution for human copyright protection in the age of generative AIs. The code is available at https://github.com/zc1023/-Plug-in-Authorization.git.