# Targeted Adversarial Examples for Black Box Audio Systems

arXiv: https://arxiv.org/abs/1805.07820 · HTML: https://mlsanity.com/paper/1805.07820/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2018.

## Built on by (most cited first, 30 of 41)

- [A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization](https://mlsanity.com/paper/2102.10707/) (2021)
- [Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information](https://mlsanity.com/paper/2110.09714/) (2021)
- [Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition](https://mlsanity.com/paper/2206.03393/) (2022)
- [Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning](https://mlsanity.com/paper/2106.00273/) (2021)
- [Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial Attack](https://mlsanity.com/paper/2110.02516/) (2021)
- [Recent improvements of ASR models in the face of adversarial attacks](https://mlsanity.com/paper/2203.16536/) (2022)
- [Muting Whisper: A Universal Acoustic Adversarial Attack on Speech Foundation Models](https://mlsanity.com/paper/2405.06134/) (2024)
- [Dompteur: Taming Audio Adversarial Examples](https://mlsanity.com/paper/2102.05431/) (2021)
- [Characterizing the adversarial vulnerability of speech self-supervised learning](https://mlsanity.com/paper/2111.04330/) (2021)
- [Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems](https://mlsanity.com/paper/2311.11796/) (2023)
- [Hijack Vertical Federated Learning Models As One Party](https://mlsanity.com/paper/2212.00322/) (2022)
- [ALIF: Low-Cost Adversarial Audio Attacks on Black-Box Speech Platforms using Linguistic Features](https://mlsanity.com/paper/2408.01808/) (2024)
- [Multi-Discriminator Sobolev Defense-GAN Against Adversarial Attacks for End-to-End Speech Systems](https://mlsanity.com/paper/2103.08086/) (2021)
- [SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems](https://mlsanity.com/paper/2103.10651/) (2021)
- [Towards Robust Speech-to-Text Adversarial Attack](https://mlsanity.com/paper/2103.08095/) (2021)
- [SEC4SR: A Security Analysis Platform for Speaker Recognition](https://mlsanity.com/paper/2109.01766/) (2021)
- [Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey](https://mlsanity.com/paper/2202.10594/) (2022)
- [Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection](https://mlsanity.com/paper/2210.00753/) (2022)
- [Security and Privacy Problems in Voice Assistant Applications: A Survey](https://mlsanity.com/paper/2304.09486/) (2023)
- [Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion Models](https://mlsanity.com/paper/2409.07936/) (2024)
- [Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning](https://mlsanity.com/paper/2211.09273/) (2022)
- [Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer](https://mlsanity.com/paper/2405.09470/) (2024)
- [Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation Models](https://mlsanity.com/paper/2407.04482/) (2024)
- [On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples](https://mlsanity.com/paper/2108.02010/) (2021)
- [Catch Me If You Can: Blackbox Adversarial Attacks on Automatic Speech Recognition using Frequency Masking](https://mlsanity.com/paper/2112.01821/) (2021)
- [Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks on Automatic Speech Recognition Systems](https://mlsanity.com/paper/2203.05408/) (2022)
- [Neural Predictor for Black-Box Adversarial Attacks on Speech Recognition](https://mlsanity.com/paper/2203.09849/) (2022)
- [Adversarial Attacks on ASR Systems: An Overview](https://mlsanity.com/paper/2208.02250/) (2022)
- [Leveraging Domain Features for Detecting Adversarial Attacks Against Deep Speech Recognition in Noise](https://mlsanity.com/paper/2211.01621/) (2022)
- [Towards the Transferable Audio Adversarial Attack via Ensemble Methods](https://mlsanity.com/paper/2304.08811/) (2023)

## 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.