# Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofing

arXiv: https://arxiv.org/abs/2007.08363 · HTML: https://mlsanity.com/paper/2007.08363/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 14 of 14)

- [Generative AI for Physical Layer Communications: A Survey](https://mlsanity.com/paper/2312.05594/) (2023)
- [Adversarial Machine Learning for 5G Communications Security](https://mlsanity.com/paper/2101.02656/) (2021)
- [Machine Learning in NextG Networks via Generative Adversarial Networks](https://mlsanity.com/paper/2203.04453/) (2022)
- [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)
- [Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications](https://mlsanity.com/paper/2109.08139/) (2021)
- [IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identification](https://mlsanity.com/paper/2201.03281/) (2022)
- [Edge Security: Challenges and Issues](https://mlsanity.com/paper/2206.07164/) (2022)
- [ViFi-Loc: Multi-modal Pedestrian Localization using GAN with Camera-Phone Correspondences](https://mlsanity.com/paper/2211.12021/) (2022)
- [Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing](https://mlsanity.com/paper/2212.13598/) (2022)
- [Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning](https://mlsanity.com/paper/2312.16715/) (2023)
- [Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning](https://mlsanity.com/paper/2412.21164/) (2024)
- [Deep Learning in Wireless Communication Receiver: A Survey](https://mlsanity.com/paper/2501.17184/) (2025)

## Abstract (arXiv)

The spoofing attack is critical to bypass physical-layer signal authentication. This paper presents a deep learning-based spoofing attack to generate synthetic wireless signals that cannot be statistically distinguished from intended transmissions. The adversary is modeled as a pair of a transmitter and a receiver that build the generator and discriminator of the generative adversarial network, respectively, by playing a minimax game over the air. The adversary transmitter trains a deep neural network to generate the best spoofing signals and fool the best defense trained as another deep neural network at the adversary receiver. Each node (defender or adversary) may have multiple transmitter or receiver antennas. Signals are spoofed by jointly capturing waveform, channel, and radio hardware effects that are inherent to wireless signals under attack. Compared with spoofing attacks using random or replayed signals, the proposed attack increases the probability of misclassifying spoofing signals as intended signals for different network topology and mobility patterns. The adversary transmitter can increase the spoofing attack success by using multiple antennas, while the attack success decreases when the defender receiver uses multiple antennas. For practical deployment, the attack implementation on embedded platforms demonstrates the low latency of generating or classifying spoofing signals.