# Cortical Features for Defense Against Adversarial Audio Attacks

arXiv: https://arxiv.org/abs/2102.00313 · HTML: https://mlsanity.com/paper/2102.00313/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 30 Jan 2021.

## Builds on (most cited first, 16 of 16)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [Adversarial Robustness Toolbox v1.0.0](https://mlsanity.com/paper/1807.01069/) (2018)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition](https://mlsanity.com/paper/1903.10346/) (2019)
- [Adversarial Music: Real World Audio Adversary Against Wake-word Detection System](https://mlsanity.com/paper/1911.00126/) (2019)
- [Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems](https://mlsanity.com/paper/1908.01551/) (2019)
- [Simple iterative method for generating targeted universal adversarial perturbations](https://mlsanity.com/paper/1911.06502/) (2019)
- [Perceptual Based Adversarial Audio Attacks](https://mlsanity.com/paper/1906.06355/) (2019)
- [Universal Adversarial Audio Perturbations](https://mlsanity.com/paper/1908.03173/) (2019)
- [Small-Footprint Open-Vocabulary Keyword Spotting with Quantized LSTM Networks](https://mlsanity.com/paper/2002.10851/) (2020)
- [Joint Time-Frequency Scattering for Audio Classification](https://mlsanity.com/paper/1512.02125/) (2015)

## Abstract (arXiv)

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.