From Profiling to Parameterization: Physics-Guided Acoustic Eavesdropping via Smartphone Accelerometers

Abstract

We present LEAKFORGE, a device-agnostic framework that converts cross-device accelerometer eavesdropping into a physics-guided data-generation problem. Crucially, device-specific leakage is not arbitrary; its dominant variation lies within a constrained family of audio-to-accelerometer transfer functions. LEAKFORGE samples this family to synthesize large-scale, device-diverse accelerometer traces from ordinary speech, explicitly modeling electromechanical transfer, structural resonances, filtering, and aliasing. An eavesdropping model trained entirely in this synthetic domain can then be applied directly to traces from previously unseen smartphones.