Invisible Ears at Your Fingertips: Acoustic Eavesdropping via Mouse Sensors

• URL: http://arxiv.org/abs/2509.13581v1
• Date: Tue, 16 Sep 2025 22:39:26 GMT
• Title: Invisible Ears at Your Fingertips: Acoustic Eavesdropping via Mouse Sensors
• Abstract summary: This paper introduces Mic-E-Mouse, the first-ever side-channel attack that targets high-performance optical mouse sensors to covertly eavesdrop on users.<n>We demonstrate that audio signals can induce subtle surface vibrations detectable by a mouse's optical sensor.<n>User-space software on popular operating systems can collect and broadcast this sensitive side channel.
• Score: 9.44089398835831
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern optical mouse sensors, with their advanced precision and high responsiveness, possess an often overlooked vulnerability: they can be exploited for side-channel attacks. This paper introduces Mic-E-Mouse, the first-ever side-channel attack that targets high-performance optical mouse sensors to covertly eavesdrop on users. We demonstrate that audio signals can induce subtle surface vibrations detectable by a mouse's optical sensor. Remarkably, user-space software on popular operating systems can collect and broadcast this sensitive side channel, granting attackers access to raw mouse data without requiring direct system-level permissions. Initially, the vibration signals extracted from mouse data are of poor quality due to non-uniform sampling, a non-linear frequency response, and significant quantization. To overcome these limitations, Mic-E-Mouse employs a sophisticated end-to-end data filtering pipeline that combines Wiener filtering, resampling corrections, and an innovative encoder-only spectrogram neural filtering technique. We evaluate the attack's efficacy across diverse conditions, including speaking volume, mouse polling rate and DPI, surface materials, speaker languages, and environmental noise. In controlled environments, Mic-E-Mouse improves the signal-to-noise ratio (SNR) by up to +19 dB for speech reconstruction. Furthermore, our results demonstrate a speech recognition accuracy of roughly 42% to 61% on the AudioMNIST and VCTK datasets. All our code and datasets are publicly accessible on https://sites.google.com/view/mic-e-mouse.

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