<ul><li>Researchers have proposed a novel attack paradigm, called Channel-Triggered Backdoor Attack (CT-BA), to exploit security vulnerabilities in wireless communication systems using deep learning.</li><li>CT-BA leverages specific wireless channels as backdoor triggers, utilizing channel gain and channel noise as potential triggers.</li><li>The attack demonstrates high success rates and effectiveness in various Semantic Communication (SemCom) systems and image reconstruction models.</li><li>The study highlights the need for developing defense mechanisms to mitigate the risks associated with such backdoor attacks on wireless communication systems.</li></ul>

A Channel-Triggered Backdoor Attack on Wireless Semantic Image Reconstruction

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