Accuth+: Accelerometer-Based Anti-Spoofing Voice Authentication on Wrist-Worn Wearables

Most existing voice-based user authentication systems mainly rely on microphones to capture the unique vocal characteristics of an individual, which are vulnerable to various acoustic attacks and may suffer high-security risks. In this work, we present Accuth(+), a novel authentication system on the wrist-worn device 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 wearable authentication approaches as it only leverages a ubiquitous, low-cost, and small-size accelerometer. In real-world experiments. Accuth(+) achieves over 92.85% averaged identification accuracy among 15 human participants and an averaged equal error rate (EER) of 1.91% for spoofing attack detection.