An Initial Investigation of Neural Replay Simulator for Over-the-Air Adversarial Perturbations to Automatic Speaker Verification

TL;DR

This paper investigates the use of a neural replay simulator to enhance the robustness of over-the-air adversarial attacks on automatic speaker verification systems, revealing increased attack success rates and raising security concerns.

Contribution

It introduces a neural waveform synthesizer to simulate the replay process, improving over-the-air attack effectiveness against speaker verification systems.

Findings

Neural replay simulator significantly boosts attack success rates.

Replay process impacts over-the-air attack effectiveness.

Study highlights security risks in physical access scenarios.

Abstract

Deep Learning has advanced Automatic Speaker Verification (ASV) in the past few years. Although it is known that deep learning-based ASV systems are vulnerable to adversarial examples in digital access, there are few studies on adversarial attacks in the context of physical access, where a replay process (i.e., over the air) is involved. An over-the-air attack involves a loudspeaker, a microphone, and a replaying environment that impacts the movement of the sound wave. Our initial experiment confirms that the replay process impacts the effectiveness of the over-the-air attack performance. This study performs an initial investigation towards utilizing a neural replay simulator to improve over-the-air adversarial attack robustness. This is achieved by using a neural waveform synthesizer to simulate the replay process when estimating the adversarial perturbations. Experiments conducted on the ASVspoof2019 dataset confirm that the neural replay simulator can considerably increase the success rates of over-the-air adversarial attacks. This raises the concern for adversarial attacks on speaker verification in physical access applications.