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An adversarial attack is an exploitative process in which minute alterationsare made to natural inputs, causing the inputs to be misclassified by neuralmodels. In the field of speech recognition, this has become an issue ofincreasing significance. Although adversarial attacks were originallyintroduced in computer vision, they have since infiltrated the realm of speechrecognition. In 2017, a genetic attack was shown to be quite potent against theSpeech Commands Model. Limited-vocabulary speech classifiers, such as theSpeech Commands Model, are used in a variety of applications, particularly intelephony; as such, adversarial examples produced by this attack pose as amajor security threat. This paper explores various methods of detecting theseadversarial examples with combinations of audio preprocessing. One particularcombined defense incorporating compressions, speech coding, filtering, andaudio panning was shown to be quite effective against the attack on the SpeechCommands Model, detecting audio adversarial examples with 93.5% precision and91.2% recall.

Isolated and Ensemble Audio Preprocessing Methods for Detecting Adversarial Examples against Automatic Speech Recognition