Blackbox adversarial attacks and explanations for automatic speech recognition

Abstract

Automatic speech recognition (ASR) models are used widely in applications for voice navigation and voice control of domestic appliances. The computational core of ASRs are Deep Neural Networks (DNNs) that have been shown to be susceptible to adversarial perturbations and exhibit unwanted biases and ethical issues. To assess the security of ASRs, we propose techniques that generate blackbox (agnostic to the DNN) adversarial attacks that are portable across ASRs. This is in contrast to existing work that focuses on whitebox attacks that are time consuming and lack portability. Apart from that, to figure out why ASRs(always blackbox) are easily attacked, we provide explanation methods on ASRs that help increase our understanding of the system and ultimately help build trust in the system.