Recognizing Voice Spoofing Attacks via Acoustic Nonlinearity Dissection for Mobile Devices

Millions of mobile devices are currently equipped with
voice assistant
(VA) for robust identity authentication. Regrettably, VA authentication remains susceptible to voice spoofing attacks, encompassing playback, synthesis, and conversion attacks. Despite numerous proposed defense schemes, these solutions exhibit deficiencies such as limited versatility and cumbersome implementation. Many are specialized in detecting only one specific type of attack, necessitate additional equipment, or mandate placing the device in specific locations. In this study, we introduce a versatile and user-friendly scheme designed to counteract voice spoofing attacks by analyzing common nonlinear features inherent in vocalization systems. Initially, we demonstrate the nonlinear nature of both human and mobile device vocalization by scrutinizing the mechanisms and processes of voice generation. Subsequently, we develop a comprehensive nonlinear model and extract a universal acoustic nonlinear property to discern sounds produced by humans from those generated by loudspeakers, thereby enhancing resistance against spoofing attacks. Finally, we conduct extensive experiments utilizing a real-world collected dataset and the supplementary ASVspoof2017 dataset. Evaluation results reveal that the proposed scheme significantly improves accuracy and computation cost by nearly 40% and 15%, respectively.