Just Accepted
Visual Content Privacy Protection: A Survey
Abstract
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.
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Publication History
Online AM: 16 December 2024
Accepted: 23 November 2024
Revised: 01 April 2024
Received: 29 March 2023
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