Generative AI has witnessed rapid advancement in recent years, expanding their capabilities to create synthesized content such as text, images, audio, and code. The high fidelity and authenticity of contents generated by these Deep Generative Models (DGMs) have sparked significant copyright concerns. There have been various legal debates on how to effectively safeguard copyrights in DGMs. This work delves into this issue by providing a comprehensive overview of copyright protection from a technical perspective. We examine from two distinct viewpoints: the copyrights pertaining to the source data held by the data owners and those of the generative models maintained by the model builders. For data copyright, we delve into methods data owners can protect their content and DGMs can be utilized without infringing upon these rights. For model copyright, our discussion extends to strategies for preventing model theft and identifying outputs generated by specific models. Finally, we highlight the limitations of existing techniques and identify areas that remain unexplored. Furthermore, we discuss prospective directions for the future of copyright protection, underscoring its importance for the sustainable and ethical development of Generative AI.

翻译：近年来，生成式人工智能迅速发展，其合成文本、图像、音频和代码等创作内容的能力不断增强。这些深度生成模型所生成内容的高度保真性与真实性引发了显著的版权问题。关于如何有效保护深度生成模型中的版权，法律界已展开多轮辩论。本文从技术视角出发，对版权保护问题进行了全面综述。我们从两个不同的角度审视该问题：一是数据所有者所持有的源数据版权，二是模型构建者所维护的生成模型的版权。在数据版权方面，我们深入探讨了数据所有者保护其内容的方法，以及如何在不侵犯这些权利的情况下使用深度生成模型。在模型版权方面，我们的讨论扩展到防止模型被盗用以及识别特定模型生成输出的策略。最后，我们指出了现有技术的局限性，并识别了尚未探索的领域。此外，我们讨论了未来版权保护的前瞻性方向，强调了其对生成式人工智能可持续与伦理发展的重要性。