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. Scholars have persistently pursued the advancement of tailored privacy protection measures. Various surveys attempt to consolidate these efforts from specific viewpoints. Nevertheless, these surveys tend to focus on particular issues, scenarios, or technologies, hindering a comprehensive overview of existing solutions on a broader scale. 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.

中文翻译：

---

视觉内容隐私保护：一项调查


视觉是人最重要的感官，也是认知的主要方式之一。因此，人们倾向于利用视觉内容来捕捉和分享他们的生活经历，这极大地促进了信息的传递。同时，它还增加了侵犯隐私的风险，例如，图像或视频可能会泄露不同类型的隐私敏感信息。学者们一直在追求量身定制的隐私保护措施的进步。各种调查试图从特定的角度整合这些努力。然而，这些调查往往侧重于特定问题、场景或技术，阻碍了在更广泛范围内对现有解决方案的全面概述。在这项调查中，提出了一个包含视觉隐私的各种关注点和解决方案的框架，该框架允许从综合层面对隐私问题进行宏观理解。它基于隐私关注有相应的对手这一事实，并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和HV对手。对于每个类别，我们分析了隐私保护的主要方法的特点，然后系统地回顾了代表性的解决方案。还讨论了视觉隐私保护的公开挑战和未来方向。