# Did you hear that? Adversarial Examples Against Automatic Speech Recognition

arXiv: https://arxiv.org/abs/1801.00554 · HTML: https://mlsanity.com/paper/1801.00554/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2018.

## Built on by (most cited first, 30 of 48)

- [WaveFake: A Data Set to Facilitate Audio Deepfake Detection](https://mlsanity.com/paper/2111.02813/) (2021)
- [A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization](https://mlsanity.com/paper/2102.10707/) (2021)
- [WaveGuard: Understanding and Mitigating Audio Adversarial Examples](https://mlsanity.com/paper/2103.03344/) (2021)
- [AdvWave: Stealthy Adversarial Jailbreak Attack against Large Audio-Language Models](https://mlsanity.com/paper/2412.08608/) (2024)
- [Grey-box Adversarial Attack And Defence For Sentiment Classification](https://mlsanity.com/paper/2103.11576/) (2021)
- [A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space](https://mlsanity.com/paper/2112.01156/) (2021)
- [Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning](https://mlsanity.com/paper/2106.00273/) (2021)
- [Recent improvements of ASR models in the face of adversarial attacks](https://mlsanity.com/paper/2203.16536/) (2022)
- [A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness](https://mlsanity.com/paper/2212.09006/) (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)
- [Exploring Targeted Universal Adversarial Perturbations to End-to-end ASR Models](https://mlsanity.com/paper/2104.02757/) (2021)
- [Practical Attacks on Voice Spoofing Countermeasures](https://mlsanity.com/paper/2107.14642/) (2021)
- [SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions](https://mlsanity.com/paper/2509.17091/) (2025)
- [PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection](https://mlsanity.com/paper/2309.06960/) (2023)
- [SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems](https://mlsanity.com/paper/2103.10651/) (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)
- [Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges](https://mlsanity.com/paper/2211.14732/) (2022)
- [DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distribution](https://mlsanity.com/paper/2305.17000/) (2023)
- [Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models](https://mlsanity.com/paper/2209.13523/) (2022)
- [Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning](https://mlsanity.com/paper/2211.09273/) (2022)
- [Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation Models](https://mlsanity.com/paper/2407.04482/) (2024)
- [ERIS: Evolutionary Real-world Interference Scheme for Jailbreaking Audio Large Models](https://mlsanity.com/paper/2509.11128/) (2025)
- [Scores Know Bobs Voice: Speaker Impersonation Attack](https://mlsanity.com/paper/2603.02781/) (2026)
- [PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attack](https://mlsanity.com/paper/2101.07538/) (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)
- [Adversarial Attacks on ASR Systems: An Overview](https://mlsanity.com/paper/2208.02250/) (2022)

## Abstract (arXiv)

Speech is a common and effective way of communication between humans, and modern consumer devices such as smartphones and home hubs are equipped with deep learning based accurate automatic speech recognition to enable natural interaction between humans and machines. Recently, researchers have demonstrated powerful attacks against machine learning models that can fool them to produceincorrect results. However, nearly all previous research in adversarial attacks has focused on image recognition and object detection models. In this short paper, we present a first of its kind demonstration of adversarial attacks against speech classification model. Our algorithm performs targeted attacks with 87% success by adding small background noise without having to know the underlying model parameter and architecture. Our attack only changes the least significant bits of a subset of audio clip samples, and the noise does not change 89% the human listener's perception of the audio clip as evaluated in our human study.