# Disappeared Command: Spoofing Attack On Automatic Speech Recognition Systems with Sound Masking

arXiv: https://arxiv.org/abs/2204.08977 · HTML: https://mlsanity.com/paper/2204.08977/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 55 references that are in the map.
- First posted on arXiv 19 Apr 2022.

## Builds on (most cited first, 27 of 27)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [One pixel attack for fooling deep neural networks](https://mlsanity.com/paper/1710.08864/) (2017)
- [Adversarial Examples for Evaluating Reading Comprehension Systems](https://mlsanity.com/paper/1707.07328/) (2017)
- [Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks](https://mlsanity.com/paper/1804.00792/) (2018)
- [Hidden Trigger Backdoor Attacks](https://mlsanity.com/paper/1910.00033/) (2019)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors](https://mlsanity.com/paper/1910.14667/) (2019)
- [Transferable Clean-Label Poisoning Attacks on Deep Neural Nets](https://mlsanity.com/paper/1905.05897/) (2019)
- [Technical Report on the CleverHans v2.1.0 Adversarial Examples Library](https://mlsanity.com/paper/1610.00768/) (2016)
- [Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition](https://mlsanity.com/paper/1903.10346/) (2019)
- [Adversarial Machine Learning -- Industry Perspectives](https://mlsanity.com/paper/2002.05646/) (2020)
- [Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding](https://mlsanity.com/paper/1808.05665/) (2018)
- [DolphinAtack: Inaudible Voice Commands](https://mlsanity.com/paper/1708.09537/) (2017)
- [CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition](https://mlsanity.com/paper/1801.08535/) (2018)
- [Black-Box Adversarial Attack with Transferable Model-based Embedding](https://mlsanity.com/paper/1911.07140/) (2019)
- [Houdini: Fooling Deep Structured Prediction Models](https://mlsanity.com/paper/1707.05373/) (2017)
- [Robust Audio Adversarial Example for a Physical Attack](https://mlsanity.com/paper/1810.11793/) (2018)
- [Hear "No Evil", See "Kenansville": Efficient and Transferable Black-Box Attacks on Speech Recognition and Voice Identification Systems](https://mlsanity.com/paper/1910.05262/) (2019)
- [Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems](https://mlsanity.com/paper/1904.05734/) (2019)
- [Targeted Attack for Deep Hashing based Retrieval](https://mlsanity.com/paper/2004.07955/) (2020)
- [Crafting Adversarial Examples For Speech Paralinguistics Applications](https://mlsanity.com/paper/1711.03280/) (2017)
- [Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information](https://mlsanity.com/paper/2110.09714/) (2021)
- [A comparison of streaming models and data augmentation methods for robust speech recognition](https://mlsanity.com/paper/2111.10043/) (2021)
- [Adversarial Example Devastation and Detection on Speech Recognition System by Adding Random Noise](https://mlsanity.com/paper/2108.13562/) (2021)

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

- [Towards the Transferable Audio Adversarial Attack via Ensemble Methods](https://mlsanity.com/paper/2304.08811/) (2023)

## Abstract (arXiv)

The development of deep learning technology has greatly promoted the performance improvement of automatic speech recognition (ASR) technology, which has demonstrated an ability comparable to human hearing in many tasks. Voice interfaces are becoming more and more widely used as input for many applications and smart devices. However, existing research has shown that DNN is easily disturbed by slight disturbances and makes false recognition, which is extremely dangerous for intelligent voice applications controlled by voice.