  The widespread use of smart devices gives rise to both security and privacy
concerns. Fingerprinting smart devices can assist in authenticating physical
devices, but it can also jeopardize privacy by allowing remote identification
without user awareness. We propose a novel fingerprinting approach that uses
the microphones and speakers of smart phones to uniquely identify an individual
device. During fabrication, subtle imperfections arise in device microphones
and speakers which induce anomalies in produced and received sounds. We exploit
this observation to fingerprint smart devices through playback and recording of
audio samples. We use audio-metric tools to analyze and explore different
acoustic features and analyze their ability to successfully fingerprint smart
devices. Our experiments show that it is even possible to fingerprint devices
that have the same vendor and model; we were able to accurately distinguish
over 93% of all recorded audio clips from 15 different units of the same model.
Our study identifies the prominent acoustic features capable of fingerprinting
devices with high success rate and examines the effect of background noise and
other variables on fingerprinting accuracy.
