Title: Noise Flooding for Detecting Audio Adversarial Examples Against Automatic Speech Recognition,
Abstract: Neural models enjoy widespread use across a variety of tasks and have grown
to become crucial components of many industrial systems. Despite their
effectiveness and extensive popularity, they are not without their exploitable
flaws. Initially applied to computer vision systems, the generation of
adversarial examples is a process in which seemingly imperceptible
perturbations are made to an image, with the purpose of inducing a deep
learning based classifier to misclassify the image. Due to recent trends in
speech processing, this has become a noticeable issue in speech recognition
models. In late 2017, an attack was shown to be quite effective against the
Speech Commands classification model. Limited-vocabulary speech classifiers,
such as the Speech Commands model, are used quite frequently in a variety of
applications, particularly in managing automated attendants in telephony
contexts. As such, adversarial examples produced by this attack could have
real-world consequences. While previous work in defending against these
adversarial examples has investigated using audio preprocessing to reduce or
distort adversarial noise, this work explores the idea of flooding particular
frequency bands of an audio signal with random noise in order to detect
adversarial examples. This technique of flooding, which does not require
retraining or modifying the model, is inspired by work done in computer vision
and builds on the idea that speech classifiers are relatively robust to natural
noise. A combined defense incorporating 5 different frequency bands for
flooding the signal with noise outperformed other existing defenses in the
audio space, detecting adversarial examples with 91.8% precision and 93.5%
recall.