Accuth+: Accelerometer-Based Anti-Spoofing Voice Authentication on Wrist-Worn Wearables
Feiyu Han, Panlong Yang, Haohua Du, Xiang-Yang Li
Abstract
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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accuth<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="han-ieq2-3314837.gif"/></alternatives></inline-formula></i> , 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. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accuth<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="han-ieq3-3314837.gif"/></alternatives></inline-formula></i> 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. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accuth<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="han-ieq4-3314837.gif"/></alternatives></inline-formula></i> 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. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accuth<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="han-ieq5-3314837.gif"/></alternatives></inline-formula></i> 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.