Adding calibrated artificial noise at the transmitter and a diffusion-model denoiser at the receiver can block semantic eavesdropping while keeping the legitimate link's reconstruction quality near baseline.
Semantic Communications With AI Tasks
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abstract
A radical paradigm shift of wireless networks from ``connected things'' to ``connected intelligence'' undergoes, which coincides with the Shanno and Weaver's envisions: Communications will transform from the technical level to the semantic level. This article proposes a semantic communication method with artificial intelligence tasks (SC-AIT). First, the architecture of SC-AIT is elaborated. Then, based on the proposed architecture, we implement SC-AIT for a image classifications task. A prototype of SC-AIT is also established for surface defect detection, is conducted. Experimental results show that SC-AIT has much lower bandwidth requirements, and can achieve more than $40\%$ classification accuracy gains compared with the communications at the technical level. Future trends and key challenges for semantic communications are also identified.
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cs.IT 1years
2025 1verdicts
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Diffusion-enabled Secure Semantic Communication Against Eavesdropping
Adding calibrated artificial noise at the transmitter and a diffusion-model denoiser at the receiver can block semantic eavesdropping while keeping the legitimate link's reconstruction quality near baseline.