# Perceptual Based Adversarial Audio Attacks

arXiv: https://arxiv.org/abs/1906.06355 · HTML: https://mlsanity.com/paper/1906.06355/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

## Built on by (most cited first, 13 of 13)

- [Sequential Randomized Smoothing for Adversarially Robust Speech Recognition](https://mlsanity.com/paper/2112.03000/) (2021)
- [Dompteur: Taming Audio Adversarial Examples](https://mlsanity.com/paper/2102.05431/) (2021)
- [Adversarial Examples in the Physical World: A Survey](https://mlsanity.com/paper/2311.01473/) (2023)
- [Multi-Discriminator Sobolev Defense-GAN Against Adversarial Attacks for End-to-End Speech Systems](https://mlsanity.com/paper/2103.08086/) (2021)
- [SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems](https://mlsanity.com/paper/2103.10651/) (2021)
- [Towards Robust Speech-to-Text Adversarial Attack](https://mlsanity.com/paper/2103.08095/) (2021)
- [Catch Me If You Can: Blackbox Adversarial Attacks on Automatic Speech Recognition using Frequency Masking](https://mlsanity.com/paper/2112.01821/) (2021)
- [Cortical Features for Defense Against Adversarial Audio Attacks](https://mlsanity.com/paper/2102.00313/) (2021)
- [SoK: A Study of the Security on Voice Processing Systems](https://mlsanity.com/paper/2112.13144/) (2021)
- [Learning Representations Robust to Group Shifts and Adversarial Examples](https://mlsanity.com/paper/2202.09446/) (2022)
- [Hearing the Unspoken: Language Model Priors for Acoustic Adversarial Attacks](https://mlsanity.com/paper/2606.06833/) (2026)
- [What Was That Again? Certified Robustness for Automatic Speech Recognition](https://mlsanity.com/paper/2606.27698/) (2026)
- [Room for Error: Large-Scale Simulation of Over-the-Air Acoustic Attacks](https://mlsanity.com/paper/2606.27701/) (2026)

## Abstract (arXiv)

Recent work has shown the possibility of adversarial attacks on automatic speechrecognition (ASR) systems. However, in the vast majority of work in this area, theattacks have been executed only in the digital space, or have involved short phrasesand static room settings. In this paper, we demonstrate a physically realizableaudio adversarial attack. We base our approach specifically on a psychoacoustic-property-based loss function, and automated generation of room impulse responses, to create adversarial attacks that are robust when played over a speaker in multiple environments. We show that such attacks are possible even while being virtually imperceptible to listeners.