Personalized and Invertible Face De-Identification by Disentangled Identity Information Manipulation
Abstract
The popularization of intelligent devices including smartphones and surveillance cameras results in more serious privacy issues. De-identification is regarded as an effective tool for visual privacy protection with the process of concealing or replacing identity information. Most of the existing de-identification methods suffer from some limitations since they mainly focus on the protection process and are usually non-reversible. In this paper, we propose a personalized and invertible de-identification method based on the deep generative model, where the main idea is introducing a user-specific password and an adjustable parameter to control the direction and degree of identity variation. Extensive experiments demonstrate the effectiveness and generalization of our proposed framework for both face de-identification and recovery.
Cite
Text
Cao et al. "Personalized and Invertible Face De-Identification by Disentangled Identity Information Manipulation." International Conference on Computer Vision, 2021. doi:10.1109/ICCV48922.2021.00332Markdown
[Cao et al. "Personalized and Invertible Face De-Identification by Disentangled Identity Information Manipulation." International Conference on Computer Vision, 2021.](https://mlanthology.org/iccv/2021/cao2021iccv-personalized/) doi:10.1109/ICCV48922.2021.00332BibTeX
@inproceedings{cao2021iccv-personalized,
title = {{Personalized and Invertible Face De-Identification by Disentangled Identity Information Manipulation}},
author = {Cao, Jingyi and Liu, Bo and Wen, Yunqian and Xie, Rong and Song, Li},
booktitle = {International Conference on Computer Vision},
year = {2021},
pages = {3334-3342},
doi = {10.1109/ICCV48922.2021.00332},
url = {https://mlanthology.org/iccv/2021/cao2021iccv-personalized/}
}