mlsanityData to 7 Oct 2026

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

arXiv 2204.08977 ↗First posted 19 Apr 2022cs.SD cs.AI eess.AScited by 1

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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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 55 references that are in the map. First posted on arXiv 19 Apr 2022.

Builds on

27 of 55 references.

References · 27

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2013Intriguing properties of neural networkscited by 5,114
  4. 2017Evasion Attacks against Machine Learning at Test Timecited by 686
  5. 2017One pixel attack for fooling deep neural networkscited by 579
  6. 2017Adversarial Examples for Evaluating Reading Comprehension Systemscited by 568
  7. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 500
  8. 2019Hidden Trigger Backdoor Attackscited by 359
  9. 2018Audio Adversarial Examples: Targeted Attacks on Speech-to-Textcited by 268
  10. 2019Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectorscited by 155
  11. 2019Transferable Clean-Label Poisoning Attacks on Deep Neural Netscited by 129
  12. 2016Technical Report on the CleverHans v2.1.0 Adversarial Examples Librarycited by 123
15 more
  1. 2019Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognitioncited by 123
  2. 2020Adversarial Machine Learning -- Industry Perspectivescited by 121
  3. 2018Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hidingcited by 83
  4. 2017DolphinAtack: Inaudible Voice Commandscited by 65
  5. 2018CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognitioncited by 63
  6. 2019Black-Box Adversarial Attack with Transferable Model-based Embeddingcited by 57
  7. 2017Houdini: Fooling Deep Structured Prediction Modelscited by 50
  8. 2018Robust Audio Adversarial Example for a Physical Attackcited by 42
  9. 2019Hear "No Evil", See "Kenansville": Efficient and Transferable Black-Box Attacks on Speech Recognition and Voice Identification Systemscited by 37
  10. 2019Practical Hidden Voice Attacks against Speech and Speaker Recognition Systemscited by 34
  11. 2020Targeted Attack for Deep Hashing based Retrievalcited by 34
  12. 2017Crafting Adversarial Examples For Speech Paralinguistics Applicationscited by 30
  13. 2021Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Informationcited by 25
  14. 2021A comparison of streaming models and data augmentation methods for robust speech recognitioncited by 4
  15. 2021Adversarial Example Devastation and Detection on Speech Recognition System by Adding Random Noisecited by 2

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Towards the Transferable Audio Adversarial Attack via Ensemble Methodscited by 1