Copyright Protection in Generative AI: A Technical Perspective
Jie Ren Han Xu Pengfei He Yingqian Cui Shenglai Zeng Jiankun Zhang Hongzhi Wen Jiayuan Ding Hui Liu Yi Chang Jiliang Tang
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.