Stop Deceiving! An Effective Defense Scheme Against Voice Impersonation Attacks on Smart Devices
Wenbin Huang, Wenjuan Tang, Hongbo Jiang, Jun Luo, Yaoxue Zhang
Abstract
Both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">voice communication</i> and automatic speech verification (ASV) over smart devices are vulnerable to the voice impersonation (VI) attack, which is often launched via imitating a target’s voice characteristics to deceive human auditory sense or fool the ASV system. Researchers have designed a number of defense schemes yet without the consideration of universality due to the lack of comprehensive data sets. In this article, we propose a universal defense scheme based on the VI data set collected from a famous TV show named “The Sound.” First, we deliver a thorough study on the VI attacks in both auditory and ASV systems to verify the collected simulated voice could spoof the auditory and the ASV system with a notable probability. Second, we propose a quasi-Gaussian distribution (QGD)-based defense scheme with the discovery about specific voice characteristics that are distinct between attackers and targets. Finally, we conduct extensive experimental results on our collected VI data set as well as the auxiliary ASVspoof2017 data set, to indicate the proposed QGD scheme outperforms the state-of-the-art schemes: backpropagation neural network, support vector machine, and Gaussian mixture model, in terms of accuracy.