  Deep generative models have demonstrated impressive performance in various
computer vision applications, including image synthesis, video generation, and
medical analysis. Despite their significant advancements, these models may be
used for malicious purposes, such as misinformation, deception, and copyright
violation. In this paper, we provide a systematic and timely review of research
efforts on defenses against AI-generated visual media, covering detection,
disruption, and authentication. We review existing methods and summarize the
mainstream defense-related tasks within a unified passive and proactive
framework. Moreover, we survey the derivative tasks concerning the
trustworthiness of defenses, such as their robustness and fairness. For each
task, we formulate its general pipeline and propose a taxonomy based on
methodological strategies that are uniformly applicable to the primary
subtasks. Additionally, we summarize the commonly used evaluation datasets,
criteria, and metrics. Finally, by analyzing the reviewed studies, we provide
insights into current research challenges and suggest possible directions for
future research.
