# The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection

arXiv: https://arxiv.org/abs/1902.10674 · HTML: https://mlsanity.com/paper/1902.10674/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 17 of 17)

- [Adversarial Machine Learning for 5G Communications Security](https://mlsanity.com/paper/2101.02656/) (2021)
- [Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network](https://mlsanity.com/paper/2101.12090/) (2021)
- [Privacy-Aware Communication Over a Wiretap Channel with Generative Networks](https://mlsanity.com/paper/2110.04094/) (2021)
- [Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing](https://mlsanity.com/paper/2101.08724/) (2021)
- [Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond](https://mlsanity.com/paper/2103.13989/) (2021)
- [Jamming Attacks on Federated Learning in Wireless Networks](https://mlsanity.com/paper/2201.05172/) (2022)
- [Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally](https://mlsanity.com/paper/2310.03614/) (2023)
- [Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning](https://mlsanity.com/paper/2107.12173/) (2021)
- [Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples](https://mlsanity.com/paper/2207.01531/) (2022)
- [Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications](https://mlsanity.com/paper/2109.08139/) (2021)
- [Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2512.12002/) (2025)
- [Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training](https://mlsanity.com/paper/2206.06592/) (2022)
- [Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learning](https://mlsanity.com/paper/2212.11778/) (2022)
- [AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery](https://mlsanity.com/paper/2309.16706/) (2023)
- [Friendly Attacks to Improve Channel Coding Reliability](https://mlsanity.com/paper/2401.14184/) (2024)
- [Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2112.11414/) (2021)
- [Covert Communication Based on the Poisoning Attack in Federated Learning](https://mlsanity.com/paper/2306.01342/) (2023)

## Abstract (arXiv)

We consider a communication scenario, in which an intruder tries to determine the modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the intruder, while guaranteeing that the intended receiver can still recover the underlying message with the highest reliability. This is achieved by perturbing channel input symbols at the encoder, similarly to adversarial attacks against classifiers in machine learning. In image classification, the perturbation is limited to be imperceptible to a human observer, while in our case the perturbation is constrained so that the message can still be reliably decoded by the legitimate receiver, which is oblivious to the perturbation. Simulation results demonstrate the viability of our approach to make wireless communication secure against state-of-the-art intruders (using deep learning or decision trees) with minimal sacrifice in the communication performance. On the other hand, we also demonstrate that using diverse training data and curriculum learning can significantly boost the accuracy of the intruder.