Deep learning based semantic communication(DLSC) systems have shown great potential of making wireless networks significantly more efficient by only transmitting the semantics of the data. However, the open nature of wireless channel and fragileness of neural models cause DLSC systems extremely vulnerable to various attacks. Traditional wireless physical layer key (PLK), which relies on reciprocal channel and randomness characteristics between two legitimate users, holds the promise of securing DLSC. The main challenge lies in generating secret keys in the static environment with ultra-low/zero rate. Different from prior efforts that use relays or reconfigurable intelligent surfaces (RIS) to manipulate wireless channels, this paper proposes a novel physical layer semantic encryption scheme by exploring the randomness of bilingual evaluation understudy (BLEU) scores in the field of machine translation, and additionally presents a novel semantic obfuscation mechanism to provide further physical layer protections. Specifically, 1) we calculate the BLEU scores and corresponding weights of the DLSC system. Then, we generate semantic keys (SKey) by feeding the weighted sum of the scores into a hash function. 2) Equipped with the SKey, our proposed subcarrier obfuscation is able to further secure semantic communications with a dynamic dummy data insertion mechanism. Experiments show the effectiveness of our method, especially in the static wireless environment.

翻译：基于深度学习的语义通信（DLSC）系统通过仅传输数据的语义，展现出显著提升无线网络效率的巨大潜力。然而，无线信道的开放特性与神经模型的脆弱性导致DLSC系统极易遭受各类攻击。传统无线物理层密钥（PLK）技术利用两个合法用户之间的互易信道和随机性特征，有望为DLSC提供安全保障，其主要挑战在于如何在超低/零速率的静态环境中生成密钥。区别于以往利用中继或可重构智能表面（RIS）操控无线信道的方法，本文通过探索机器翻译领域双语评估替代（BLEU）分数的随机性，提出一种新型物理层语义加密方案，并进一步引入语义混淆机制以增强物理层防护。具体而言：1）我们计算DLSC系统的BLEU分数及其对应权重，将加权求和后的分数输入哈希函数生成语义密钥（SKey）；2）基于所提SKey，我们设计的子载波混淆机制通过动态伪数据插入机制进一步保障语义通信安全。实验证明，该方法在静态无线环境中尤其具有显著有效性。