VoIPLoc: passive VoIP call provenance using acoustic side-channels

Shishir Nagaraja, Ryan Shah

Research output: Contribution to conferencePaperpeer-review

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We propose VoIPLoc, a novel location fingerprinting technique and apply it to the VoIP call provenance problem. It exploits echo-location information embedded within VoIP audio to support fine-grained location inference. We found consistent statistical features induced by the echo-reflection characteristics of the location into recorded speech. These features are discernible within traces received at the VoIP destination, enabling location inference. We evaluated VoIPLoc by developing a dataset of audio traces received through VoIP channels over the Tor network. We show that recording locations can be fingerprinted and detected remotely with a low false-positive rate, even when a majority of the audio samples are unlabelled. Finally, we note that the technique is fully passive and thus undetectable, unlike prior art. VoIPLoc is robust to the impact of environmental noise and background sounds, as well as the impact of compressive codecs and network jitter. The technique is also highly scalable and offers several degrees of freedom terms of the fingerprintable space.
Original languageEnglish
Number of pages19
Publication statusAccepted/In press - 5 May 2021
Event14th ACM Conference on Security and Privacy in Wireless and Mobile Networks 2021 - Virtual Event. Hosted by New York University, Abu Dhabi, United Arab Emirates
Duration: 28 Jun 20212 Jul 2021
Conference number: 14th


Conference14th ACM Conference on Security and Privacy in Wireless and Mobile Networks 2021
Abbreviated titleACM WiSec 2021
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi


  • VoIP security
  • call provenance
  • source identification
  • location privacy
  • acoustic fingerprint


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