mlsanityData to 7 Oct 2026

Targeted Adversarial Examples for Black Box Audio Systems

arXiv 1805.07820 ↗First posted 20 May 2018cs.LG cs.CR cs.SDcited by 41

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Abstract (arXiv)

The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems. Many have demonstrated that small adversarial perturbations can fool deep neural networks into incorrectly predicting a specified target with high confidence. Current work on fooling ASR systems have focused on white-box attacks, in which the model architecture and parameters are known. In this paper, we adopt a black-box approach to adversarial generation, combining the approaches of both genetic algorithms and gradient estimation to solve the task. We achieve a 89.25% targeted attack similarity after 3000 generations while maintaining 94.6% audio file similarity.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2018.

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Citing papers per year: 2021: 13; 2022: 12; 2023: 3; 2024: 6; 2025: 4; 2026: 3.

41 papers.

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  1. 2021A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimizationcited by 46
  2. 2021Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Informationcited by 25
  3. 2022Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognitioncited by 22
  4. 2021Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learningcited by 16
  5. 2021Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial Attackcited by 15
  6. 2022Recent improvements of ASR models in the face of adversarial attackscited by 13
  7. 2024Muting Whisper: A Universal Acoustic Adversarial Attack on Speech Foundation Modelscited by 12
  8. 2021Dompteur: Taming Audio Adversarial Examplescited by 10
  9. 2023Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systemscited by 6
  10. 2021Characterizing the adversarial vulnerability of speech self-supervised learningcited by 5
  11. 2024ALIF: Low-Cost Adversarial Audio Attacks on Black-Box Speech Platforms using Linguistic Featurescited by 5
  12. 2021Multi-Discriminator Sobolev Defense-GAN Against Adversarial Attacks for End-to-End Speech Systemscited by 4
29 more
  1. 2021SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systemscited by 4
  2. 2022Hijack Vertical Federated Learning Models As One Partycited by 4
  3. 2021Towards Robust Speech-to-Text Adversarial Attackcited by 3
  4. 2021SEC4SR: A Security Analysis Platform for Speaker Recognitioncited by 3
  5. 2022Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Surveycited by 3
  6. 2022Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detectioncited by 3
  7. 2022Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learningcited by 2
  8. 2023Security and Privacy Problems in Voice Assistant Applications: A Surveycited by 2
  9. 2024Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfercited by 2
  10. 2024Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation Modelscited by 2
  11. 2024Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion Modelscited by 2
  12. 2021On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examplescited by 1
  13. 2021Catch Me If You Can: Blackbox Adversarial Attacks on Automatic Speech Recognition using Frequency Maskingcited by 1
  14. 2022Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks on Automatic Speech Recognition Systemscited by 1
  15. 2022Neural Predictor for Black-Box Adversarial Attacks on Speech Recognitioncited by 1
  16. 2022Adversarial Attacks on ASR Systems: An Overviewcited by 1
  17. 2022Leveraging Domain Features for Detecting Adversarial Attacks Against Deep Speech Recognition in Noisecited by 1
  18. 2023Towards the Transferable Audio Adversarial Attack via Ensemble Methodscited by 1
  19. 2024D-CAPTCHA++: A Study of Resilience of Deepfake CAPTCHA under Transferable Imperceptible Adversarial Attackcited by 1
  20. 2025Investigating Vulnerabilities and Defenses Against Audio-Visual Attacks: A Comprehensive Survey Emphasizing Multimodal Modelscited by 1
  21. 2026I Guess That's Why They Call it the Blues: Causal Analysis for Audio Classifierscited by 1
  22. 2021Context-Aware Image Denoising with Auto-Threshold Canny Edge Detection to Suppress Adversarial Perturbationcited by 0
  23. 2022Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learningcited by 0
  24. 2022RSD-GAN: Regularized Sobolev Defense GAN Against Speech-to-Text Adversarial Attackscited by 0
  25. 2025ASRJam: Human-Friendly AI Speech Jamming to Prevent Automated Phone Scamscited by 0
  26. 2025IGAff: Benchmarking Adversarial Iterative and Genetic Affine Algorithms on Deep Neural Networkscited by 0
  27. 2025Toward Understanding the Transferability of Adversarial Suffixes in Large Language Modelscited by 0
  28. 2026Clustering Unsupervised Representations as Defense against Poisoning Attacks on Speech Commands Classification Systemcited by 0
  29. 2026Generative Testing of Automated Speech Recognition Systemscited by 0