# Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems

arXiv: https://arxiv.org/abs/2102.00918 · HTML: https://mlsanity.com/paper/2102.00918/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2021.

## Built on by (most cited first, 12 of 12)

- ["Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice](https://mlsanity.com/paper/2212.14315/) (2022)
- [Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey](https://mlsanity.com/paper/2410.23687/) (2024)
- [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)
- [Universal Adversarial Attacks on Neural Networks for Power Allocation in a Massive MIMO System](https://mlsanity.com/paper/2110.04731/) (2021)
- [Robust Universal Adversarial Perturbations](https://mlsanity.com/paper/2206.10858/) (2022)
- [Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems](https://mlsanity.com/paper/2311.00207/) (2023)
- [Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers](https://mlsanity.com/paper/2404.15344/) (2024)
- [Transfer-based Adversarial Poisoning Attacks for Online (MIMO-)Deep Receviers](https://mlsanity.com/paper/2409.02430/) (2024)
- [Repair Brain Damage: Real-Numbered Error Correction Code for Neural Network](https://mlsanity.com/paper/2602.00076/) (2026)
- [On the Fragility of AI-Based Channel Decoders under Small Channel Perturbations](https://mlsanity.com/paper/2602.01582/) (2026)
- [CITADEL: CSI-Based Jamming Detection and Open-Set Classification for IIoT Networks](https://mlsanity.com/paper/2606.22939/) (2026)

## Abstract (arXiv)

Deep Neural Networks (DNNs) have become prevalent in wireless communication systems due to their promising performance. However, similar to other DNN-based applications, they are vulnerable to adversarial examples. In this work, we propose an input-agnostic, undetectable, and robust adversarial attack against DNN-based wireless communication systems in both white-box and black-box scenarios. We design tailored Universal Adversarial Perturbations (UAPs) to perform the attack. We also use a Generative Adversarial Network (GAN) to enforce an undetectability constraint for our attack. Furthermore, we investigate the robustness of our attack against countermeasures. We show that in the presence of defense mechanisms deployed by the communicating parties, our attack performs significantly better compared to existing attacks against DNN-based wireless systems. In particular, the results demonstrate that even when employing well-considered defenses, DNN-based wireless communications are vulnerable to adversarial attacks.