Visual Content Privacy Protection: A Survey

by   Ruoyu Zhao, et al.

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.


page 3

page 5

page 10

page 12

page 13

page 14

page 17


A Survey on Patients Privacy Protection with Stganography and Visual Encryption

In this survey, thirty models for steganography and visual encryption me...

Cardea: Context-Aware Visual Privacy Protection from Pervasive Cameras

The growing popularity of mobile and wearable devices with built-in came...

Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions

Recent advances in machine learning have enabled its wide application in...

Security and Privacy on Generative Data in AIGC: A Survey

The advent of artificial intelligence-generated content (AIGC) represent...

Conducting Privacy-Sensitive Surveys: A Case Study of Civil Society Organizations

Compared to other organizations, civil society organizations (CSOs) ofte...

Machine Unlearning: A Survey

Machine learning has attracted widespread attention and evolved into an ...

A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection

Deep graph learning has achieved remarkable progresses in both business ...

Please sign up or login with your details

Forgot password? Click here to reset