Practical Earphone Eavesdropping with Built-in Motion Sensors

The rising popularity of ear-wear devices equipped with motion sensors has brought concerns regarding privacy issues due to their powerful sensing capabilities. Previous studies have shown the potential for speech eavesdropping using earphone motion sensors with a sampling frequency of 1000 Hz. However, as the risks of such attacks continue to escalate, mobile operating systems like Android have imposed limitations on the sampling frequency, typically no more than 200 Hz, to avoid such attacks. The lower sampling frequency reduces the amount of collected information within the same timeframe, potentially leading to decreased accuracy. In this paper, we further investigate the effectiveness of utilizing earphone motion sensors for inferring sensitive information at a sampling frequency of 200 Hz while directly using raw data without any data transformation to prevent information loss. We employ a channel attention mechanism to dynamically adjust axis weights to address the varying energy levels across different sensor axes. Meanwhile, we analyze the impact of sampling frequency, environment, and volume on speech recognition performance. Additionally, we explore the extraction of other information from speech signals, such as speaker identity and gender. Our experiments on two datasets demonstrate high recognition accuracy for all three tasks at the 200Hz sampling frequency. We expect our work to raise awareness among manufacturers regarding the privacy issues associated with earphone motion sensors.