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arXiv 2505.05018cs.ITmath.IT
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基于扩散模型的抗窃听安全语义通信

Diffusion-enabled Secure Semantic Communication Against Eavesdropping

Boxiang He, Zihan Chen, Fanggang Wang, Shilian Wang, Zhijin Qin, Tony Q. S. Quek

AI总结:

本文首次将人工噪声引入语义通信系统抗窃听,利用去噪扩散概率模型设计可插拔加密与解密模块,并针对发射机是否已知窃听者知识两种场景分别提出基于深度强化学习和对抗残差网络的噪声生成方法,在防止语义窃听的同时保证高质量语义通信。

摘要:

本文首次将人工噪声(AN)引入语义通信系统以防止语义窃听。然而,AN的引入也给合法接收方提取语义信息带来了挑战。近年来,去噪扩散概率模型(DDPM)在生成多媒体内容方面展现出强大能力。本文利用DDPM精心设计了成对的可插拔模块。具体而言,可插拔加密模块生成AN并将其叠加到语义发射机的输出上,而位于语义接收机之前的可插拔解密模块利用DDPM通过同时去除AN和信道噪声来生成详细的语义信息。在发射机不具备窃听者知识的场景中,采用人工高斯噪声(AGN)作为AN。我们首先建立了一个功率分配优化问题来确定AGN的功率,其目标是最小化合法链路的数据重构误差、非法链路的互信息以及信道输入失真的加权和。随后,提出了一种基于深度确定性策略梯度(DDPG)的深度强化学习框架来求解该优化问题。在发射机已知窃听者知识的场景中,我们提出了一种基于对抗残差网络(ARN)的AN生成方法。与前一场景不同,目标函数中的互信息项被替换为窃听者正确恢复私有信息的置信度。然后训练对抗残差网络以最小化修改后的目标函数。仿真结果表明,基于扩散模型的可插拔加密模块能够有效防止语义窃听,同时可插拔解密模块实现了高质量的语义通信。

英文摘要:

In this paper, AN is introduced into semantic communication systems for the first time 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. The simulation results show that the diffusion-enabled pluggable encryption module prevents semantic eavesdropping while the pluggable decryption module achieves the high-quality semantic communication.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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