# AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery

arXiv: https://arxiv.org/abs/2309.16706 · HTML: https://mlsanity.com/paper/2309.16706/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 60 references that are in the map.
- First posted on arXiv 17 Aug 2023.

## Builds on (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [Deep Learning Methods for Improved Decoding of Linear Codes](https://mlsanity.com/paper/1706.07043/) (2017)
- [Learning to Decode Linear Codes Using Deep Learning](https://mlsanity.com/paper/1607.04793/) (2016)
- [DeepRx: Fully Convolutional Deep Learning Receiver](https://mlsanity.com/paper/2005.01494/) (2020)
- [Adversarial Attacks on Deep-Learning Based Radio Signal Classification](https://mlsanity.com/paper/1808.07713/) (2018)
- [6G Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2108.04973/) (2021)
- [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)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors](https://mlsanity.com/paper/2305.11239/) (2023)
- [IoT Network Security from the Perspective of Adversarial Deep Learning](https://mlsanity.com/paper/1906.00076/) (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)
- [Spectrum Data Poisoning with Adversarial Deep Learning](https://mlsanity.com/paper/1901.09247/) (2019)
- [When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing](https://mlsanity.com/paper/1905.01430/) (2019)
- [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)
- [Adversarial Examples in RF Deep Learning: Detection of the Attack and its Physical Robustness](https://mlsanity.com/paper/1902.06044/) (2019)
- [Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-training](https://mlsanity.com/paper/1902.08034/) (2019)
- ["Machine LLRning": Learning to Softly Demodulate](https://mlsanity.com/paper/1907.01512/) (2019)
- [DeepWiPHY: Deep Learning-based Receiver Design and Dataset for IEEE 802.11ax Systems](https://mlsanity.com/paper/2010.09268/) (2020)
- [Deep Learning based Channel Estimation Algorithm over Time Selective Fading Channels](https://mlsanity.com/paper/1908.11013/) (2019)
- [Adversarial Machine Learning Attack on Modulation Classification](https://mlsanity.com/paper/1909.12167/) (2019)
- [DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer](https://mlsanity.com/paper/2003.14124/) (2020)

## Built on by (most cited first, 1 of 1)

- [On the Fragility of AI-Based Channel Decoders under Small Channel Perturbations](https://mlsanity.com/paper/2602.01582/) (2026)

## Abstract (arXiv)

A wireless communications system usually consists of a transmitter which transmits the information and a receiver which recovers the original information from the received distorted signal. Deep learning (DL) has been used to improve the performance of the receiver in complicated channel environments and state-of-the-art (SOTA) performance has been achieved. However, its robustness has not been investigated. In order to evaluate the robustness of DL-based information recovery models under adversarial circumstances, we investigate adversarial attacks on the SOTA DL-based information recovery model, i.e., DeepReceiver. We formulate the problem as an optimization problem with power and peak-to-average power ratio (PAPR) constraints. We design different adversarial attack methods according to the adversary's knowledge of DeepReceiver's model and/or testing samples. Extensive experiments show that the DeepReceiver is vulnerable to the designed attack methods in all of the considered scenarios. Even in the scenario of both model and test sample restricted, the adversary can attack the DeepReceiver and increase its bit error rate (BER) above 10%. It can also be found that the DeepReceiver is vulnerable to adversarial perturbations even with very low power and limited PAPR. These results suggest that defense measures should be taken to enhance the robustness of DeepReceiver.