Looking through walls

Inferring scenes from video-surveillance encrypted traffic

Journal article (2021)

Authors

Daniele Mari Università degli Studi di Padova
Sara Bordin Università degli Studi di Padova
Luca Pajola Università degli Studi di Padova
Sebastiano Verde Università degli Studi di Padova
Simone Milani Università degli Studi di Padova
M. Conti Università degli Studi di Padova
Affiliation
External organisation
Transfer learning Video surveillance Encrypted channel Information leakage Traffic analysis
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Published Date

2021

Language

English

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Affiliation

External organisation

Abstract

Nowadays living environments are characterized by networks of interconnected 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.