# Low-Interception Waveform: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples

arXiv: https://arxiv.org/abs/2201.08731 · HTML: https://mlsanity.com/paper/2201.08731/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 20 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses](https://mlsanity.com/paper/1811.09600/) (2018)
- [Adversarial Attacks on Deep-Learning Based Radio Signal Classification](https://mlsanity.com/paper/1808.07713/) (2018)
- [Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/1901.07947/) (2019)
- [Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels](https://mlsanity.com/paper/2002.02400/) (2020)
- [Investigating a Spectral Deception Loss Metric for Training Machine Learning-based Evasion Attacks](https://mlsanity.com/paper/2005.13124/) (2020)

## Abstract (arXiv)

Deep learning is applied to many complex tasks in the field of wireless communication, such as modulation recognition of spectrum waveforms, because of its convenience and efficiency. This leads to the problem of a malicious third party using a deep learning model to easily recognize the modulation format of the transmitted waveform. Some existing works address this problem directly using the concept of adversarial examples in the image domain without fully considering the characteristics of the waveform transmission in the physical world. Therefore, we propose a low-intercept waveform~(LIW) generation method that can reduce the probability of the modulation being recognized by a third party without affecting the reliable communication of the friendly party. Our LIW exhibits significant low-interception performance even in the physical hardware experiment, decreasing the accuracy of the state of the art model to approximately 15% with small perturbations.