# Adversarial Attacks on Deep-Learning Based Radio Signal Classification

arXiv: https://arxiv.org/abs/1808.07713 · HTML: https://mlsanity.com/paper/1808.07713/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2018.

## Built on by (most cited first, 30 of 47)

- [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)
- [ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?](https://mlsanity.com/paper/2303.05382/) (2023)
- [Machine Learning in NextG Networks via Generative Adversarial Networks](https://mlsanity.com/paper/2203.04453/) (2022)
- [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)
- [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)
- [Countermeasures Against Adversarial Examples in Radio Signal Classification](https://mlsanity.com/paper/2407.06796/) (2024)
- [Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications](https://mlsanity.com/paper/2109.08139/) (2021)
- [Universal Adversarial Attacks on Neural Networks for Power Allocation in a Massive MIMO System](https://mlsanity.com/paper/2110.04731/) (2021)
- [Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge](https://mlsanity.com/paper/2110.14357/) (2021)
- [Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks](https://mlsanity.com/paper/2205.15743/) (2022)
- [Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses](https://mlsanity.com/paper/2210.13289/) (2022)
- [A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classification](https://mlsanity.com/paper/2407.06807/) (2024)
- [On the benefits of robust models in modulation recognition](https://mlsanity.com/paper/2103.14977/) (2021)
- [SafeAMC: Adversarial training for robust modulation recognition models](https://mlsanity.com/paper/2105.13746/) (2021)
- [Radio Signal Classification by Adversarially Robust Quantum Machine Learning](https://mlsanity.com/paper/2312.07821/) (2023)
- [Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification](https://mlsanity.com/paper/2501.01620/) (2025)
- [Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices](https://mlsanity.com/paper/2506.11892/) (2025)
- [Vision Transformer with Adversarial Indicator Token against Adversarial Attacks in Radio Signal Classifications](https://mlsanity.com/paper/2507.00015/) (2025)
- [Adapt under Attack and Domain Shift: Unified Adversarial Meta-Learning and Domain Adaptation for Robust Automatic Modulation Classification](https://mlsanity.com/paper/2511.01172/) (2025)
- [Real-time Over-the-air Adversarial Perturbations for Digital Communications using Deep Neural Networks](https://mlsanity.com/paper/2202.11197/) (2022)
- [Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training](https://mlsanity.com/paper/2206.06592/) (2022)
- [Universal Fourier Attack for Time Series](https://mlsanity.com/paper/2209.00757/) (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)
- [Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems](https://mlsanity.com/paper/2311.00207/) (2023)
- [Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning](https://mlsanity.com/paper/2312.16715/) (2023)

## Abstract (arXiv)

Deep learning (DL), despite its enormous success in many computer vision and language processing applications, is exceedingly vulnerable to adversarial attacks. We consider the use of DL for radio signal (modulation) classification tasks, and present practical methods for the crafting of white-box and universal black-box adversarial attacks in that application. We show that these attacks can considerably reduce the classification performance, with extremely small perturbations of the input. In particular, these attacks are significantly more powerful than classical jamming attacks, which raises significant security and robustness concerns in the use of DL-based algorithms for the wireless physical layer.