保护隐私的同时保持人体姿态估计精度，且可逆恢复敏感信息
Recoverable Anonymization for Pose Estimation: A Privacy-Enhancing Approach
- 设计可逆隐私增强模块，保留上下文信息
- 在保持高姿态估计精度下实现敏感信息保护
- 适合需要可恢复隐私的安防与监控场景
人体姿态估计（HPE）在诸多应用中至关重要。然而，在监控场景部署HPE算法会引发显著隐私问题，因可能泄露面部特征、种族等敏感个人身份信息（SPI）。现有隐私增强方法常在隐私与性能间妥协，或需额外昂贵模态。本文提出一种新型隐私增强系统，可在生成隐私化肖像的同时保持高精度的HPE表现。核心创新包括：为授权人员提供敏感信息的可逆恢复机制，以及对上下文信息的充分保留。通过联合优化隐私增强模块、隐私恢复模块与姿态估计算法，系统实现强隐私保护、高效敏感信息恢复及高性能姿态估计。实验结果表明，该系统在隐私增强、敏感信息恢复与姿态估计方面均表现出鲁棒性。
原文摘要 · Abstract (English)
Human pose estimation (HPE) is crucial for various applications. However, deploying HPE algorithms in surveillance contexts raises significant privacy concerns due to the potential leakage of sensitive personal information (SPI) such as facial features, and ethnicity. Existing privacy-enhancing methods often compromise either privacy or performance, or they require costly additional modalities. We propose a novel privacy-enhancing system that generates privacy-enhanced portraits while maintaining high HPE performance. Our key innovations include the reversible recovery of SPI for authorized personnel and the preservation of contextual information. By jointly optimizing a privacy-enhancing module, a privacy recovery module, and a pose estimator, our system ensures robust privacy protection, efficient SPI recovery, and high-performance HPE. Experimental results demonstrate the system's robust performance in privacy enhancement, SPI recovery, and HPE.
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