CVPR 2017, 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, 21-26 July 2017, Honolulu, Hawaii, USA
The 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 deanonymization attacks. Moreover, the impact on the source coding stream is negligible.
Type:
Conference
City:
Honolulu
Date:
2017-07-21
Department:
Digital Security
Eurecom Ref:
5283
Copyright:
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