# Visual Content Privacy Protection: A Survey

arXiv: https://arxiv.org/abs/2303.16552 · HTML: https://mlsanity.com/paper/2303.16552/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2023.

## Built on by (most cited first, 11 of 11)

- [Privacy-preserving Optics for Enhancing Protection in Face De-identification](https://mlsanity.com/paper/2404.00777/) (2024)
- [Red-Teaming Segment Anything Model](https://mlsanity.com/paper/2404.02067/) (2024)
- [CAPED: Context-Aware Privacy Exposure Defense for Mobile GUI Agents](https://mlsanity.com/paper/2606.12666/) (2026)
- [Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy Versus Performance](https://mlsanity.com/paper/2410.18717/) (2024)
- [OpenConstruction: A Systematic Synthesis of Open Visual Datasets for Data-Centric Artificial Intelligence in Construction Monitoring](https://mlsanity.com/paper/2508.11482/) (2025)
- [Responsible AI for General-Purpose Systems: Overview, Challenges, and A Path Forward](https://mlsanity.com/paper/2601.13122/) (2026)
- [PrivHAR-Bench: A Graduated Privacy Benchmark Dataset for Video-Based Action Recognition](https://mlsanity.com/paper/2604.00761/) (2026)
- [VPD-100K: Towards Generalizable and Fine-grained Visual Privacy Protection](https://mlsanity.com/paper/2605.10229/) (2026)
- [Private Face Recognition Training Dataset Publication via Identity-Decoupled and Geometry-Preserving Face Distillation](https://mlsanity.com/paper/2607.27764/) (2026)
- [RF-HOI: Recognize Human-Object Interaction with Radio Frequency Signals](https://mlsanity.com/paper/2608.00289/) (2026)
- [Anti-Persona: Disrupting Unauthorized Identity Binding and Recognition in Personalized Vision--Language Models](https://mlsanity.com/paper/2610.01944/) (2026)

## Abstract (arXiv)

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.