Graduate School and Research Center in Digital Sciences

ASePPI: Robust privacy protection against de-anonymization attacks

Ruchaud, Natacha; Dugelay, Jean-Luc

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.

Document Doi Hal Bibtex

Title:ASePPI: Robust privacy protection against de-anonymization attacks
Department:Digital Security
Eurecom ref:5283
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Bibtex: @inproceedings{EURECOM+5283, doi = {}, year = {2017}, title = {{AS}e{PPI}: {R}obust privacy protection against de-anonymization attacks}, author = {{R}uchaud, {N}atacha and {D}ugelay, {J}ean-{L}uc}, booktitle = {{CVPR} 2017, 30th {IEEE} {C}onference on {C}omputer {V}ision and {P}attern {R}ecognition {W}orkshops, 21-26 {J}uly 2017, {H}onolulu, {H}awaii, {USA} }, address = {{H}onolulu, {UNITED} {STATES}}, month = {07}, url = {} }
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