# Adversarial Machine Learning Attack on Modulation Classification

arXiv: https://arxiv.org/abs/1909.12167 · HTML: https://mlsanity.com/paper/1909.12167/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Gradient-based Adversarial Deep Modulation Classification with Data-driven Subsampling](https://mlsanity.com/paper/2104.06375/) (2021)
- [Radio Signal Classification by Adversarially Robust Quantum Machine Learning](https://mlsanity.com/paper/2312.07821/) (2023)
- [AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery](https://mlsanity.com/paper/2309.16706/) (2023)
- [Data-Driven Subsampling in the Presence of an Adversarial Actor](https://mlsanity.com/paper/2401.03488/) (2024)

## Abstract (arXiv)

Modulation classification is an important component of cognitive self-driving networks. Recently many ML-based modulation classification methods have been proposed. We have evaluated the robustness of 9 ML-based modulation classifiers against the powerful Carlini & Wagner (C-W) attack and showed that the current ML-based modulation classifiers do not provide any deterrence against adversarial ML examples. To the best of our knowledge, we are the first to report the results of the application of the C-W attack for creating adversarial examples against various ML models for modulation classification.