Downsampling Attack on Automatic Speaker Authentication System

Abstract

Recent years have observed an exponential growth in the popularity of audio-based authentication systems. The benefit of a voice-based authentication system is that the person need not be physically present. Voice biometric system provides effective authentication in various domains like remote access control, authentication in mobile applications, customer care centers for call attests. Most of the existing authentication systems that recognize speakers formulate deep learning models for better classification. At the same time, research studies show that deep learning models are highly vulnerable to adversarial inputs. A breach in security on authentication systems are not generally acceptable. This paper exposes the vulnerabilities of audio-based authentication systems. Here, we propose a novel downsampling attack to the speaker recognition system. This attack can effectively trick the speaker recognition framework by causing inaccurate predictions. The proposed threat model achieved remarkable attack effectiveness of 75%. This system employs a custom human voice dataset recorded in real-time conditions to achieve real-time effectiveness during classification. We compare the attack accuracy of the proposed attack against the adversarial audios generated using the CleverHans toolbox. The proposed attack being a black box attack, is transferable to other deep learning systems also.