# Leveraging Domain Features for Detecting Adversarial Attacks Against Deep Speech Recognition in Noise

arXiv: https://arxiv.org/abs/2211.01621 · HTML: https://mlsanity.com/paper/2211.01621/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 3 Nov 2022.

## Builds on (most cited first, 19 of 19)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [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)
- [CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition](https://mlsanity.com/paper/1801.08535/) (2018)
- [Houdini: Fooling Deep Structured Prediction Models](https://mlsanity.com/paper/1707.05373/) (2017)
- [Did you hear that? Adversarial Examples Against Automatic Speech Recognition](https://mlsanity.com/paper/1801.00554/) (2018)
- [Exploring Neural Transducers for End-to-End Speech Recognition](https://mlsanity.com/paper/1707.07413/) (2017)
- [Targeted Adversarial Examples for Black Box Audio Systems](https://mlsanity.com/paper/1805.07820/) (2018)
- [Fooling End-to-end Speaker Verification by Adversarial Examples](https://mlsanity.com/paper/1801.03339/) (2018)
- [Characterizing Audio Adversarial Examples Using Temporal Dependency](https://mlsanity.com/paper/1809.10875/) (2018)
- [rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method](https://mlsanity.com/paper/1906.03588/) (2019)
- [ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio](https://mlsanity.com/paper/1805.11852/) (2018)
- [Noise Flooding for Detecting Audio Adversarial Examples Against Automatic Speech Recognition](https://mlsanity.com/paper/1812.10061/) (2018)
- [Adversarial Example Detection by Classification for Deep Speech Recognition](https://mlsanity.com/paper/1910.10013/) (2019)
- [Identifying Audio Adversarial Examples via Anomalous Pattern Detection](https://mlsanity.com/paper/2002.05463/) (2020)
- [Isolated and Ensemble Audio Preprocessing Methods for Detecting Adversarial Examples against Automatic Speech Recognition](https://mlsanity.com/paper/1809.04397/) (2018)
- [DNN Filter Bank Cepstral Coefficients for Spoofing Detection](https://mlsanity.com/paper/1702.03791/) (2017)

## Built on by (most cited first, 1 of 1)

- [Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion Models](https://mlsanity.com/paper/2409.07936/) (2024)

## Abstract (arXiv)

In recent years, significant progress has been made in deep model-based automatic speech recognition (ASR), leading to its widespread deployment in the real world. At the same time, adversarial attacks against deep ASR systems are highly successful. Various methods have been proposed to defend ASR systems from these attacks. However, existing classification based methods focus on the design of deep learning models while lacking exploration of domain specific features. This work leverages filter bank-based features to better capture the characteristics of attacks for improved detection. Furthermore, the paper analyses the potentials of using speech and non-speech parts separately in detecting adversarial attacks. In the end, considering adverse environments where ASR systems may be deployed, we study the impact of acoustic noise of various types and signal-to-noise ratios. Extensive experiments show that the inverse filter bank features generally perform better in both clean and noisy environments, the detection is effective using either speech or non-speech part, and the acoustic noise can largely degrade the detection performance.