Accuth: Anti-Spoofing Voice Authentication via Accelerometer

Most existing voice-based user authentication systems mainly rely on microphones to capture the unique vocal characteristics of an individual, which makes these systems vulnerable to various acoustic attacks and suffer high-security risks. In this work, we present Accuth, a novel authentication system that takes advantage of a low-cost accelerometer to verify the user's identity and resist spoofing acoustic attacks. Accuth captures unique sound vibrations during the human pronunciation process and extracts multi-level features to verify the user's identity. Specifically, we analyze and model the differences between the physical sound field of human beings and loudspeakers, and extract a novel sound-field-level liveness feature to defend against spoofing attacks. Accuth is an effective complement to existing authentication approaches as it only leverages a ubiquitous, low-cost, and small-size accelerometer. In real-world experiments, Accuth achieves over 90% identification accuracy among 15 human participants and an average equal error rate (EER) of 3.02% for spoofing attack detection.