Abstract: In recent years, the rapid advancement of deep learning technology has introduced innovative solutions to the field of facial de-identification. Compared to traditional image processing techniques, deep generative models have demonstrated significant advantages in this domain, including high-quality image generation and robust model performance. This article reviews and synthesizes the theoretical explorations and research outcomes of deep learning technology in addressing facial de-identification challenges. It begins by outlining the network architectures and fundamental principles employed in deep learning for facial de-identification. It then delves into the de-identification methods based on these technologies, covering key techniques such as facial swapping and feature perturbation, and introduces the standard experimental metrics used to evaluate these methods. Furthermore, the article summarizes the main challenges currently faced by the technology, such as the stability of posture and expression, attribute disentanglement, and the adaptability to video applications, and looks forward to the pressing issues that future research needs to address. Ultimately, this article emphasizes the importance of deep learning technology in the field of facial de-identification and points out the direction for future research. It aims to provide readers with in-depth insights into the field of facial de-identification and inspire new ideas and directions for future studies.