The article "Copyright Protection in Generative AI: A Technical Perspective" by Jie Ren, Han Xu, Pengfei He, Yingqian Cui, Shenglai Zeng, Jiankun Zhang, Hongzhi Wen, Jiayuan Ding, Pei Huang, Lingjuan Lyu, Hui Liu, Yi Chang, and Jiliang Tang explores the challenges and technical solutions for copyright protection in Deep Generative Models (DGMs). The authors examine two main aspects: copyright protection for source data owners and model builders. For data owners, they discuss methods such as crafting unrecognizable examples, watermarks, machine unlearning, dataset de-duplication, and alignment to prevent unauthorized use of their data. For model builders, they focus on strategies like watermarking to track ownership and prevent model theft. The article also highlights the limitations of existing techniques and suggests future directions for improving copyright protection in generative AI, emphasizing the importance of sustainable and ethical development. The introduction provides background on popular image generation models, including Autoencoders, GANs, and Diffusion Models, and discusses the copyright issues they pose. The main sections delve into data copyright protection techniques, including unrecognizable examples, watermarks, and machine unlearning, with detailed explanations of various methods and their effectiveness. The article concludes by addressing the limitations of current approaches and proposing areas for future research.The article "Copyright Protection in Generative AI: A Technical Perspective" by Jie Ren, Han Xu, Pengfei He, Yingqian Cui, Shenglai Zeng, Jiankun Zhang, Hongzhi Wen, Jiayuan Ding, Pei Huang, Lingjuan Lyu, Hui Liu, Yi Chang, and Jiliang Tang explores the challenges and technical solutions for copyright protection in Deep Generative Models (DGMs). The authors examine two main aspects: copyright protection for source data owners and model builders. For data owners, they discuss methods such as crafting unrecognizable examples, watermarks, machine unlearning, dataset de-duplication, and alignment to prevent unauthorized use of their data. For model builders, they focus on strategies like watermarking to track ownership and prevent model theft. The article also highlights the limitations of existing techniques and suggests future directions for improving copyright protection in generative AI, emphasizing the importance of sustainable and ethical development. The introduction provides background on popular image generation models, including Autoencoders, GANs, and Diffusion Models, and discusses the copyright issues they pose. The main sections delve into data copyright protection techniques, including unrecognizable examples, watermarks, and machine unlearning, with detailed explanations of various methods and their effectiveness. The article concludes by addressing the limitations of current approaches and proposing areas for future research.