Spearphone: A Speech Privacy Exploit via Accelerometer-Sensed Reverberations from Smartphone Loudspeakers

By Abhishek Anand, Chen Wang, Jian Liu, Nitesh Saxena, Yingying Chen. 2019

29 citations recorded by the source corpus.

In this paper, we build a speech privacy attack that exploits speech reverberations generated from a smartphone's inbuilt loudspeaker captured via a zero-permission motion sensor (accelerometer). We design our attack, called Spearphone2, and demonstrate that speech reverberations from inbuilt loudspeakers, at an appropriate loudness, can impact the accelerometer, leaking sensitive information about the speech. In particular, we show that by exploiting the affected accelerometer readings and carefully selecting feature sets along with off-the-shelf machine learning techniques, Spearphone can successfully perform gender classification (accuracy over 90%) and speaker identification (accuracy over 80%). In addition, we perform speech recognition and speech reconstruction to extract more information about the eavesdropped speech to an extent. Our work brings to light a fundamental design vulnerability in many currently-deployed smartphones, which may put people's speech privacy at risk while using the smartphone in the loudspeaker mode during phone calls, media playback or voice assistant interactions.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

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