Summary of Recoverable Anonymization For Pose Estimation: a Privacy-enhancing Approach, by Wenjun Huang et al.
Recoverable Anonymization for Pose Estimation: A Privacy-Enhancing Approach
by Wenjun Huang, Yang Ni, Arghavan Rezvani, SungHeon Jeong, Hanning Chen, Yezi Liu, Fei Wen, Mohsen Imani
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel privacy-enhancing system generates privacy-enhanced portraits while maintaining high Human Pose Estimation (HPE) performance. It jointly optimizes a privacy-enhancing module, a privacy recovery module, and a pose estimator to ensure robust privacy protection, efficient SPI recovery, and high-performance HPE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This system is designed to prevent the leakage of sensitive personal information (SPI) such as facial features and ethnicity in surveillance contexts. By preserving contextual information and recovering SPI for authorized personnel, it provides a balance between privacy and performance. |
Keywords
» Artificial intelligence » Pose estimation
