  We propose using a computational model of the auditory cortex as a defense
against adversarial attacks on audio. We apply several white-box iterative
optimization-based adversarial attacks to an implementation of Amazon Alexa's
HW network, and a modified version of this network with an integrated cortical
representation, and show that the cortical features help defend against
universal adversarial examples. At the same level of distortion, the
adversarial noises found for the cortical network are always less effective for
universal audio attacks. We make our code publicly available at
https://github.com/ilyakava/py3fst.
