# Hidden Backdoor Attack against Deep Learning-Based Wireless Signal Modulation Classifiers

arXiv: https://arxiv.org/abs/2306.10753 · HTML: https://mlsanity.com/paper/2306.10753/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2023.

## Built on by (most cited first, 2 of 2)

- [Targeted Attacks and Defenses for Distributed Federated Learning in Vehicular Networks](https://mlsanity.com/paper/2510.15109/) (2025)
- [Model Forensics in AI-Native Wireless Networks: Taxonomy, Applications, and Case Study](https://mlsanity.com/paper/2605.14387/) (2026)

## Abstract (arXiv)

Recently, DL has been exploited in wireless communications such as modulation classification. However, due to the openness of wireless channel and unexplainability of DL, it is also vulnerable to adversarial attacks. In this correspondence, we investigate a so called hidden backdoor attack to modulation classification, where the adversary puts elaborately designed poisoned samples on the basis of IQ sequences into training dataset. These poisoned samples are hidden because it could not be found by traditional classification methods. And poisoned samples are same to samples with triggers which are patched samples in feature space. We show that the hidden backdoor attack can reduce the accuracy of modulation classification significantly with patched samples. At last, we propose activation cluster to detect abnormal samples in training dataset.