# A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classification

arXiv: https://arxiv.org/abs/2407.06807 · HTML: https://mlsanity.com/paper/2407.06807/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 15 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [MMA Training: Direct Input Space Margin Maximization through Adversarial Training](https://mlsanity.com/paper/1812.02637/) (2018)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [CAT: Customized Adversarial Training for Improved Robustness](https://mlsanity.com/paper/2002.06789/) (2020)
- [Adversarial Attacks on Deep-Learning Based Radio Signal Classification](https://mlsanity.com/paper/1808.07713/) (2018)
- [Fast Deep Learning for Automatic Modulation Classification](https://mlsanity.com/paper/1901.05850/) (2019)
- [Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid](https://mlsanity.com/paper/1708.06939/) (2017)
- [Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?](https://mlsanity.com/paper/1910.11585/) (2019)
- [A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification](https://mlsanity.com/paper/2104.03494/) (2021)
- [Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-training](https://mlsanity.com/paper/1902.08034/) (2019)
- [Classification of Radio Signals and HF Transmission Modes with Deep Learning](https://mlsanity.com/paper/1906.04459/) (2019)

## Built on by (most cited first, 3 of 3)

- [Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classification](https://mlsanity.com/paper/2408.05151/) (2024)
- [Vision Transformer with Adversarial Indicator Token against Adversarial Attacks in Radio Signal Classifications](https://mlsanity.com/paper/2507.00015/) (2025)
- [A Novel and Practical Universal Adversarial Perturbations against Deep Reinforcement Learning based Intrusion Detection Systems](https://mlsanity.com/paper/2511.18223/) (2025)

## Abstract (arXiv)

Motivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applying deep neural network for devising future generations of wireless networks. However, several recent works have pointed out that imperceptible and carefully designed adversarial examples (attacks) can significantly deteriorate the classification accuracy. In this paper, we investigate a defense mechanism based on both training-time and run-time defense techniques for protecting machine learning-based radio signal (modulation) classification against adversarial attacks. The training-time defense consists of adversarial training and label smoothing, while the run-time defense employs a support vector machine-based neural rejection (NR). Considering a white-box scenario and real datasets, we demonstrate that our proposed techniques outperform existing state-of-the-art technologies.