# Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-training

arXiv: https://arxiv.org/abs/1902.08034 · HTML: https://mlsanity.com/paper/1902.08034/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2019.

## Built on by (most cited first, 7 of 7)

- [Adversarial Machine Learning for 5G Communications Security](https://mlsanity.com/paper/2101.02656/) (2021)
- [Jamming Attacks on Federated Learning in Wireless Networks](https://mlsanity.com/paper/2201.05172/) (2022)
- [Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning](https://mlsanity.com/paper/2107.12173/) (2021)
- [A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classification](https://mlsanity.com/paper/2407.06807/) (2024)
- [AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery](https://mlsanity.com/paper/2309.16706/) (2023)
- [DAFAR: Defending against Adversaries by Feedback-Autoencoder Reconstruction](https://mlsanity.com/paper/2103.06487/) (2021)
- [A look at adversarial attacks on radio waveforms from discrete latent space](https://mlsanity.com/paper/2506.09896/) (2025)

## Abstract (arXiv)

Adversarial examples in machine learning for images are widely publicized and explored. Illustrations of misclassifications caused by slightly perturbed inputs are abundant and commonly known (e.g., a picture of panda imperceptibly perturbed to fool the classifier into incorrectly labeling it as a gibbon). Similar attacks on deep learning (DL) for radio frequency (RF) signals and their mitigation strategies are scarcely addressed in the published work. Yet, RF adversarial examples (AdExs) with minimal waveform perturbations can cause drastic, targeted misclassification results, particularly against spectrum sensing/survey applications (e.g. BPSK is mistaken for 8-PSK). Our research on deep learning AdExs and proposed defense mechanisms are RF-centric, and incorporate physical world, over-the-air (OTA) effects. We herein present defense mechanisms based on pre-training the target classifier using an autoencoder. Our results validate this approach as a viable mitigation method to subvert adversarial attacks against deep learning-based communications and radar sensing systems.