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.

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