# Over-the-air White-box Attack on the Wav2Vec Speech Recognition Neural Network

arXiv: https://arxiv.org/abs/2603.16972 · HTML: https://mlsanity.com/paper/2603.16972/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 6 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition](https://mlsanity.com/paper/1903.10346/) (2019)
- [Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding](https://mlsanity.com/paper/1808.05665/) (2018)
- [Zero-Query Adversarial Attack on Black-box Automatic Speech Recognition Systems](https://mlsanity.com/paper/2406.19311/) (2024)
- [An Integrated Algorithm for Robust and Imperceptible Audio Adversarial Examples](https://mlsanity.com/paper/2310.03349/) (2023)

## Abstract (arXiv)

Automatic speech recognition systems based on neural networks are vulnerable to adversarial attacks that alter transcriptions in a malicious way. Recent works in this field have focused on making attacks work in over-the-air scenarios, however such attacks are typically detectable by human hearing, limiting their potential applications. In the present work we explore different approaches of making over-the-air attacks less detectable, as well as the impact these approaches have on the attacks' effectiveness.