mlsanityData to 7 Oct 2026

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

arXiv 2603.16972 ↗First posted 17 Mar 2026eess.AS cs.LG cs.SDcited by 0

Not on the map. Why?

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 6 references that are in the map. First posted on arXiv 17 Mar 2026.

Builds on

6 of 6 references.

References · 6

  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  2. 2018Audio Adversarial Examples: Targeted Attacks on Speech-to-Textcited by 268
  3. 2019Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognitioncited by 123
  4. 2018Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hidingcited by 83
  5. 2024Zero-Query Adversarial Attack on Black-box Automatic Speech Recognition Systemscited by 10
  6. 2023An Integrated Algorithm for Robust and Imperceptible Audio Adversarial Examplescited by 1

Built on by

Not cited yet.