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ASePPI: Robust Privacy Protection Against De-Anonymization Attacks
AbstractThe evolution of the video surveillance systems generates questions concerning protection of individual privacy. In this paper, we design ASePPI, an Adaptive Scrambling enabling Privacy Protection and Intelligibility method operating in the H.264/AVC stream with the aim to be robust against de-anonymization attacks targeting the restoration of the original image and the re-identification of people. The proposed approach automatically adapts the level of protection according to the resolution of the region of interest. Compared to existing methods, our framework provides a better trade-off between the privacy protection and the visibility of the scene with robustness against de-anonymization attacks. Moreover, the impact on the source coding stream is negligible.
Related Material
[pdf][bibtex]@InProceedings{Dugelay_2017_CVPR_Workshops,
author = {Ruchaud; Jean-Luc Dugelay, Natacha},
title = {ASePPI: Robust Privacy Protection Against De-Anonymization Attacks},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}
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