A Channel-Triggered Backdoor Attack on Wireless Semantic Image Reconstruction
This addresses security vulnerabilities in wireless semantic communications, particularly for image-oriented applications, and is incremental by focusing on a novel trigger method rather than a new paradigm.
The paper tackles the problem of backdoor attacks in semantic communication systems for image reconstruction by proposing a channel-triggered attack that uses wireless channel characteristics as triggers, achieving near-perfect attack success rates in simulations.
This paper investigates backdoor attacks in image-oriented semantic communications. The threat of backdoor attacks on symbol reconstruction in semantic communication (SemCom) systems has received limited attention. Previous research on backdoor attacks targeting SemCom symbol reconstruction primarily focuses on input-level triggers, which are impractical in scenarios with strict input constraints. In this paper, we propose a novel channel-triggered backdoor attack (CT-BA) framework that exploits inherent wireless channel characteristics as activation triggers. Our key innovation involves utilizing fundamental channel statistics parameters, specifically channel gain with different fading distributions or channel noise with different power, as potential triggers. This approach enhances stealth by eliminating explicit input manipulation, provides flexibility through trigger selection from diverse channel conditions, and enables automatic activation via natural channel variations without adversary intervention. We extensively evaluate CT-BA across four joint source-channel coding (JSCC) communication system architectures and three benchmark datasets. Simulation results demonstrate that our attack achieves near-perfect attack success rate (ASR) while maintaining effective stealth. Finally, we discuss potential defense mechanisms against such attacks.