# ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio

arXiv: https://arxiv.org/abs/1805.11852 · HTML: https://mlsanity.com/paper/1805.11852/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2018.

## Built on by (most cited first, 12 of 12)

- [Muting Whisper: A Universal Acoustic Adversarial Attack on Speech Foundation Models](https://mlsanity.com/paper/2405.06134/) (2024)
- [Interactive Machine Learning: A State of the Art Review](https://mlsanity.com/paper/2207.06196/) (2022)
- [Universal Acoustic Adversarial Attacks for Flexible Control of Speech-LLMs](https://mlsanity.com/paper/2505.14286/) (2025)
- [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)
- [SEC4SR: A Security Analysis Platform for Speaker Recognition](https://mlsanity.com/paper/2109.01766/) (2021)
- [Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception](https://mlsanity.com/paper/2207.13192/) (2022)
- [Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation Models](https://mlsanity.com/paper/2407.04482/) (2024)
- [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)
- [Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning](https://mlsanity.com/paper/2204.02381/) (2022)
- [RSD-GAN: Regularized Sobolev Defense GAN Against Speech-to-Text Adversarial Attacks](https://mlsanity.com/paper/2207.06858/) (2022)

## Abstract (arXiv)

Adversarial machine learning research has recently demonstrated the feasibility to confuse automatic speech recognition (ASR) models by introducing acoustically imperceptible perturbations to audio samples. To help researchers and practitioners gain better understanding of the impact of such attacks, and to provide them with tools to help them more easily evaluate and craft strong defenses for their models, we present ADAGIO, the first tool designed to allow interactive experimentation with adversarial attacks and defenses on an ASR model in real time, both visually and aurally. ADAGIO incorporates AMR and MP3 audio compression techniques as defenses, which users can interactively apply to attacked audio samples. We show that these techniques, which are based on psychoacoustic principles, effectively eliminate targeted attacks, reducing the attack success rate from 92.5% to 0%. We will demonstrate ADAGIO and invite the audience to try it on the Mozilla Common Voice dataset.