# Black-box Adversarial ML Attack on Modulation Classification

## TL;DR

This paper evaluates the robustness of deep neural network-based modulation classifiers against black-box adversarial attacks using the Carlini & Wagner method, revealing their vulnerability and highlighting the need for improved security measures.

## Contribution

First to assess the robustness of CNN and LSTM modulation classifiers against C-W adversarial attacks in black-box settings.

## Key findings

- State-of-the-art classifiers are vulnerable to C-W attacks.
- Adversarial examples significantly degrade classification accuracy.
- Highlights the necessity for robust defense mechanisms.

## Abstract

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.