Vision is the most important sense for people, and it is also one of the main ways of cognition. As a result, people tend to utilize visual content to capture and share their life experiences, which greatly facilitates the transfer of information. Meanwhile, it also increases the risk of privacy violations, e.g., an image or video can reveal different kinds of privacy-sensitive information. Researchers have been working continuously to develop targeted privacy protection solutions, and there are several surveys to summarize them from certain perspectives. However, these surveys are either problem-driven, scenario-specific, or technology-specific, making it difficult for them to summarize the existing solutions in a macroscopic way. In this survey, a framework that encompasses various concerns and solutions for visual privacy is proposed, which allows for a macro understanding of privacy concerns from a comprehensive level. It is based on the fact that privacy concerns have corresponding adversaries, and divides privacy protection into three categories, based on computer vision (CV) adversary, based on human vision (HV) adversary, and based on CV \& HV adversary. For each category, we analyze the characteristics of the main approaches to privacy protection, and then systematically review representative solutions. Open challenges and future directions for visual privacy protection are also discussed.

翻译：视觉是人类最重要的感官，也是认知的主要途径之一。因此，人们倾向于利用视觉内容来捕捉和分享生活经历，这极大地促进了信息传递。与此同时，这也增加了隐私泄露的风险，例如，一张图像或一段视频可能泄露多种隐私敏感信息。研究人员持续致力于开发针对性的隐私保护方案，已有若干综述从特定视角进行总结。然而，这些综述或基于问题驱动，或针对特定场景，或聚焦于特定技术，难以从宏观层面概括现有解决方案。本文提出一个涵盖视觉隐私各类问题与解决方案的框架，可从综合层面宏观理解隐私关切。该框架基于隐私关切存在对应对手这一事实，将隐私保护分为三类：基于计算机视觉（CV）对手、基于人类视觉（HV）对手，以及基于CV与HV联合对手。针对每一类别，本文分析主要隐私保护方法的特点，进而系统回顾代表性解决方案。最后，讨论视觉隐私保护面临的开放性挑战与未来方向。