# Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces

arXiv: https://arxiv.org/abs/2112.11414 · HTML: https://mlsanity.com/paper/2112.11414/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 33 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 27 of 27)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Adversarial Attacks on Deep-Learning Based Radio Signal Classification](https://mlsanity.com/paper/1808.07713/) (2018)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [Deep Learning for Wireless Communications](https://mlsanity.com/paper/2005.06068/) (2020)
- [Wireless Communications Through Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1906.09490/) (2019)
- [Deep Learning for Launching and Mitigating Wireless Jamming Attacks](https://mlsanity.com/paper/1807.02567/) (2018)
- [Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications](https://mlsanity.com/paper/1903.01563/) (2019)
- [Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers](https://mlsanity.com/paper/2005.05321/) (2020)
- [Adversarial Machine Learning in Wireless Communications using RF Data: A Review](https://mlsanity.com/paper/2012.14392/) (2020)
- [Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning](https://mlsanity.com/paper/1904.10136/) (2019)
- [Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks](https://mlsanity.com/paper/1911.00500/) (2019)
- [The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection](https://mlsanity.com/paper/1902.10674/) (2019)
- [Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels](https://mlsanity.com/paper/2002.02400/) (2020)
- [Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning](https://mlsanity.com/paper/1910.10766/) (2019)
- [Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifiers](https://mlsanity.com/paper/2006.14576/) (2020)
- [Indoor Signal Focusing with Deep Learning Designed Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1905.07726/) (2019)
- [When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions](https://mlsanity.com/paper/2001.08883/) (2020)
- [Deep Reinforcement Learning for Intelligent Reflecting Surfaces: Towards Standalone Operation](https://mlsanity.com/paper/2002.11101/) (2020)
- [A New Wireless Communication Paradigm through Software-controlled Metasurfaces](https://mlsanity.com/paper/1806.01792/) (2018)
- [Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers](https://mlsanity.com/paper/2007.16204/) (2020)
- [Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers](https://mlsanity.com/paper/2012.02160/) (2020)
- [How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacy](https://mlsanity.com/paper/2005.07675/) (2020)
- [Deep Learning for THz Drones with Flying Intelligent Surfaces: Beam and Handoff Prediction](https://mlsanity.com/paper/2102.11222/) (2021)
- [Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications](https://mlsanity.com/paper/2109.08139/) (2021)
- [Intelligent Spectrum Learning for Wireless Networks with Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2103.01531/) (2021)
- [Autoencoder-based Communications with Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2112.04441/) (2021)

## Abstract (arXiv)

By moving from massive antennas to antenna surfaces for software-defined wireless systems, the reconfigurable intelligent surfaces (RISs) rely on arrays of unit cells to control the scattering and reflection profiles of signals, mitigating the propagation loss and multipath attenuation, and thereby improving the coverage and spectral efficiency. In this paper, covert communication is considered in the presence of the RIS. While there is an ongoing transmission boosted by the RIS, both the intended receiver and an eavesdropper individually try to detect this transmission using their own deep neural network (DNN) classifiers. The RIS interaction vector is designed by balancing two (potentially conflicting) objectives of focusing the transmitted signal to the receiver and keeping the transmitted signal away from the eavesdropper. To boost covert communications, adversarial perturbations are added to signals at the transmitter to fool the eavesdropper's classifier while keeping the effect on the receiver low. Results from different network topologies show that adversarial perturbation and RIS interaction vector can be jointly designed to effectively increase the signal detection accuracy at the receiver while reducing the detection accuracy at the eavesdropper to enable covert communications.