©Plug-in Authorization for Human Content Copyright Protection
  in Text-to-Image Model
Chao Zhou Huishuai Zhang Jiang Bian Weiming Zhang Nenghai Yu
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.