Diffusion-Enabled Secure Semantic Communication Against Eavesdropping

doi:10.1109/TWC.2026.3670608

Diffusion-Enabled Secure Semantic Communication Against Eavesdropping

2026-03-11

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DOI：10.1109/TWC.2026.3670608

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This paper proposes a novel diffusion-enabled pluggable encryption/decryption modules design against semantic eavesdropping, where the pluggable modules are optionally assembled into the semantic communication system for preventing eavesdropping. Inspired by the artificial noise (AN)-based security schemes in traditional wireless communication systems, in this paper, AN is introduced into semantic communication systems to prevent semantic eavesdropping. However, the introduction of AN also poses challenges for the legitimate receiver in extracting semantic information. Recently, denoising diffusion probabilistic models (DDPM) have demonstrated their powerful capabilities in generating multimedia content. Here, the paired pluggable modules are carefully designed using DDPM. Specifically, the pluggable encryption module generates AN and adds it to the output of the semantic transmitter, while the pluggable decryption module before semantic receiver uses DDPM to generate the detailed semantic information by removing both AN and the channel noise. In the scenario where the transmitter lacks eavesdropper’s knowledge, the artificial Gaussian noise (AGN) is used as AN. We first model a power allocation optimization problem to determine the power of AGN, in which the objective is to minimize the weighted sum of data reconstruction error of legal link, the mutual information of illegal link, and the channel input distortion. Then, a deep reinforcement learning framework using deep deterministic policy gradient is proposed to solve the optimization problem. In the scenario where the transmitter is aware of the eavesdropper’s knowledge, we propose an AN generation method based on adversarial residual networks (ARN). Unlike the previous scenario, the mutual information term in the objective function is replaced by the confidence of eavesdropper correctly retrieving private information. The adversarial residual network is then trained to minimize the modified objective function. Simulation results show that the diffusion-enabled pluggable encryption module prevents semantic eavesdropping with high covertness while the pluggable decryption module achieves the high-quality semantic communication.

Keywords:

Artificial noise

diffusion models

semantic communication

wireless security

Diffusion-Enabled Secure Semantic Communication Against Eavesdropping

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