# Weighted-Sampling Audio Adversarial Example Attack

arXiv: https://arxiv.org/abs/1901.10300 · HTML: https://mlsanity.com/paper/1901.10300/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2019.

## Built on by (most cited first, 3 of 3)

- [Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer](https://mlsanity.com/paper/2405.09470/) (2024)
- [Sparse Tokens Suffice: Jailbreaking Audio Language Models via Token-Aware Gradient Optimization](https://mlsanity.com/paper/2605.04700/) (2026)
- [Rigid Body Adversarial Attacks](https://mlsanity.com/paper/2502.05669/) (2025)

## Abstract (arXiv)

Recent studies have highlighted audio adversarial examples as a ubiquitous threat to state-of-the-art automatic speech recognition systems. Thorough studies on how to effectively generate adversarial examples are essential to prevent potential attacks. Despite many research on this, the efficiency and the robustness of existing works are not yet satisfactory. In this paper, we propose~textitweighted-sampling audio adversarial examples, focusing on the numbers and the weights of distortion to reinforce the attack. Further, we apply a denoising method in the loss function to make the adversarial attack more imperceptible. Experiments show that our method is the first in the field to generate audio adversarial examples with low noise and high audio robustness at the minute time-consuming level.