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.

翻译：本文探讨了文本生成图像模型所生成图像中版权侵权的争议性问题，引发了人工智能开发者、内容创作者及法律实体之间的激烈辩论。当前最先进的模型能够生成高质量内容，却未注明原始创作者，引发了艺术界的广泛担忧。为缓解这一问题，我们提出了©插件授权框架，引入三种操作：添加、提取与组合。添加操作通过训练特定版权的©插件，实现合理的署名归属；提取操作使创作者能够从侵权模型中收回版权；组合操作则允许用户合并不同的©插件。这些操作如同许可证，激励合理使用并赋予授权灵活性。我们提出了创新方法：用于提取的"逆向LoRA"技术和用于无缝组合的"EasyMerge"技术。在艺术家风格复制与卡通IP再现实验中的结果表明，©插件能有效保护人类版权，为生成式AI时代的版权保护提供了宝贵的解决方案。