InertiEAR: Automatic and Device-independent IMU-based Eavesdropping on Smartphones

IMU-based eavesdropping has brought growing concerns over smartphone users’ privacy. In such attacks, adversaries utilize IMUs that require zero permissions for access to acquire speeches. A common countermeasure is to limit sampling rates (within 200 Hz) to reduce overlap of vocal fundamental bands (85-255 Hz) and inertial measurements (0-100 Hz). Nevertheless, we experimentally observe that IMUs sampling below 200 Hz still record adequate speech-related information because of aliasing distortions. Accordingly, we propose a practical side-channel attack, InertiEAR, to break the defense of sampling rate restriction on the zero-permission eavesdropping. It leverages IMUs to eavesdrop on both top and bottom speakers in smartphones. In the InertiEAR design, we exploit coherence between responses of the built-in accelerometer and gyroscope and their hardware diversity using a mathematical model. The coherence allows precise segmentation without manual assistance. We also mitigate the impact of hardware diversity and achieve better device-independent performance than existing approaches that have to massively increase training data from different smartphones for a scalable network model. These two advantages re-enable zero-permission attacks but also extend the attacking surface and endangering degree to off-the-shelf smartphones. InertiEAR achieves a recognition accuracy of 78.8% with a cross-device accuracy of up to 49.8% among 12 smartphones.