@inproceedings{
kuang2024latent,
title={Latent Representation Reorganization for Face Privacy Protection},
author={Zhenzhong Kuang and Jianan Lu and Chenhui Hong and Haobin Huang and Suguo Zhu and Xiaowei Zhao and Jun Yu and Jianping Fan},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=gFXXDAZZjL}
}
The issue of face privacy protection has aroused wide social concern along with the increasing applications of face images. The latest methods focus on achieving a good privacy-utility tradeoff so that the protected results can still be used to support the downstream computer vision tasks. However, they may suffer from limited flexibility in manipulating this tradeoff because the practical requirements may vary under different scenarios. In this paper, we present a two-stage latent representation reorganization (LReOrg) framework for face image privacy protection relying on our conditional bidirectional network which is optimized by using a distinct keyword-based swap training strategy with a multi-task loss. The privacy sensitive information are anonymized in the first stage and the destroyed useful information are recovered in the second stage according to user requirements. LReOrg is advantageous in: (a) enabling users to recurrently process fine-grained attributes; (b) providing flexible control over privacy-utility tradeoff by manipulating which attributes to anonymize or preserve using cross-modal keywords; and (c) eliminating the need of data annotations for network training. The experimental results on benchmark datasets have reported the superior ability of our approach for providing flexible protection on facial information.
Latent Representation Reorganization for Face Privacy Protection
Zhenzhong Kuang Jianan Lu Chenhui Hong Haobin Huang Suguo Zhu Xiaowei Zhao Jun Yu Jianping Fan