# Black-box Adversarial ML Attack on Modulation Classification

arXiv: https://arxiv.org/abs/1908.00635 · HTML: https://mlsanity.com/paper/1908.00635/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 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)
- [Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge](https://mlsanity.com/paper/2110.14357/) (2021)
- [Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses](https://mlsanity.com/paper/2210.13289/) (2022)
- [QUASAR: A Quantum-Classical Neural Network for SAR Satellite Physical-Layer Authentication](https://mlsanity.com/paper/2608.20240/) (2026)

## Abstract (arXiv)

Recently, many deep neural networks (DNN) based modulation classification schemes have been proposed in the literature. We have evaluated the robustness of two famous such modulation classifiers (based on the techniques of convolutional neural networks and long short term memory) against adversarial machine learning attacks in black-box settings. We have used Carlini & Wagner (C-W) attack for performing the adversarial attack. To the best of our knowledge, the robustness of these modulation classifiers has not been evaluated through C-W attack before. Our results clearly indicate that state-of-art deep machine learning-based modulation classifiers are not robust against adversarial attacks.