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Looking through Walls: Inferring Scenes from Video-Surveillance Encrypted Traffic

Citation Author(s):
Daniele Mari, Samuele Giuliano Piazzetta, Sara Bordin, Luca Pajola, Sebastiano Verde, Simone Milani, Mauro Conti
Submitted by:
Sebastiano Verde
Last updated:
22 June 2021 - 10:34am
Document Type:
Document Year:
Presenters Name:
Sebastiano Verde
Paper Code:



Nowadays living environments are characterized by networks of inter-connected sensing devices that accomplish different tasks, e.g., video-surveillance of an environment by a network of CCTV cameras. A malicious user could gather sensitive details on people’s activities by eavesdropping the exchanged data packets. To overcome this problem,video streams are protected by encryption systems, but even secured channels may still leak some information. In this paper, we show that it is possible to infer visual data by intercepting the encrypted video stream of a surveillance system, and how this may be leveraged to track the movements of a person inside the secured area. We trained an automatic classifier on a computer graphic simulator and tested it on real videos, with standard encryption protocols. Experiments proved the transferability of the classifier trained on synthetic sequences, succeeding in the detection of up to four different walking directions on real videos, with a limited amount of intercepted traffic.

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