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.

Looking through walls: Inferring scenes from video-surveillance encrypted traffic

Mari D.;Piazzetta S. G.;Pajola L.;Verde S.;Milani S.;Conti M.
2021

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.
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
978-1-7281-7605-5
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3402948
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact