CSBA: Covert Semantic Backdoor Attack Against Intelligent Connected Vehicles

Semantic communication (SemCom) can reduce data traffic for intelligent connected vehicles (ICVs), given the limited wireless spectrum available. However, it is important to recognize that deep learning-based SemCom is vulnerable to backdoor attacks, which pose significant security risks to ICVs. Therefore, it is crucial to investigate these security risks before integrating SemCom into ICVs. To this end, this study introduces a novel backdoor attack known as Covert Semantic Backdoor Attack (CSBA), specifically designed for SemCom-enabled ICVs. Unlike existing backdoor attack techniques that rely on noticeable triggers, CSBA analyzes the self-contained semantics in transmitted images to determine if they contain the target semantic required for initiating a backdoor attack. Moreover, in the event of an attack by CSBA, the target semantics disappear from the recovered image while the rest of the image remains unchanged, ensuring that the attack remains invisible. Experimental results confirm the effectiveness and the stealthiness of the proposed CSBA schemes across various wireless channel conditions and attack ratios.