# Backdoor Attacks and Defenses on Semantic-Symbol Reconstruction in Semantic Communications

arXiv: https://arxiv.org/abs/2404.13279 · HTML: https://mlsanity.com/paper/2404.13279/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2024.

## Built on by (most cited first, 5 of 5)

- [Imperceptible Rhythm Backdoor Attacks: Exploring Rhythm Transformation for Embedding Undetectable Vulnerabilities on Speech Recognition](https://mlsanity.com/paper/2406.10932/) (2024)
- [A Channel-Triggered Backdoor Attack on Wireless Semantic Image Reconstruction](https://mlsanity.com/paper/2503.23866/) (2025)
- [Detecting Semantic Backdoors in a Mystery Shopping Scenario](https://mlsanity.com/paper/2601.03805/) (2026)
- [Toward Polymorphic Backdoor against Semantic Communication via Intensity-Based Poisoning](https://mlsanity.com/paper/2604.23231/) (2026)
- [Wireless Backdoor Attack and Defense for Semantic Communications over Multiple Access Channel](https://mlsanity.com/paper/2606.30595/) (2026)

## Abstract (arXiv)

Semantic communication is of crucial importance for the next-generation wireless communication networks. The existing works have developed semantic communication frameworks based on deep learning. However, systems powered by deep learning are vulnerable to threats such as backdoor attacks and adversarial attacks. This paper delves into backdoor attacks targeting deep learning-enabled semantic communication systems. Since current works on backdoor attacks are not tailored for semantic communication scenarios, a new backdoor attack paradigm on semantic symbols (BASS) is introduced, based on which the corresponding defense measures are designed. Specifically, a training framework is proposed to prevent BASS. Additionally, reverse engineering-based and pruning-based defense strategies are designed to protect against backdoor attacks in semantic communication. Simulation results demonstrate the effectiveness of both the proposed attack paradigm and the defense strategies.