Fingerprinting Smart Devices Through Embedded Acoustic Components
This addresses security and privacy concerns for smart device users by enabling device authentication but also posing risks of remote identification without awareness.
The paper tackles the problem of uniquely identifying smart devices by exploiting subtle imperfections in their microphones and speakers through audio playback and recording, achieving over 93% accuracy in distinguishing 15 units of the same model.
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.