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.

翻译：我们建议使用听觉皮层的计算模型来防御对音频的对抗性攻击。我们对亚马逊亚历山大HW网络的实施采用几种白箱迭代优化对抗性攻击,并采用经修改的这一网络,并配有综合性皮层代表,同时表明皮层特征有助于抵御普遍的对抗性例子。在同样的扭曲程度,为皮层网络发现的对立性噪音对于普遍音频攻击来说总是不太有效。我们在https://github.com/ilyakava/py3fst上公开我们的代码。