Recently proliferated deep learning-based semantic communications (DLSC) focus on how transmitted symbols efficiently convey a desired meaning to the destination. However, the sensitivity of neural models and the openness of wireless channels cause the DLSC system to be extremely fragile to various malicious attacks. This inspires us to ask a question: ``Can we further exploit the advantages of transmission efficiency in wireless semantic communications while also alleviating its security disadvantages?''. Keeping this in mind, we propose SemEntropy, a novel method that answers the above question by exploring the semantics of data for both adaptive transmission and physical layer encryption. Specifically, we first introduce semantic entropy, which indicates the expectation of various semantic scores regarding the transmission goal of the DLSC. Equipped with such semantic entropy, we can dynamically assign informative semantics to Orthogonal Frequency Division Multiplexing (OFDM) subcarriers with better channel conditions in a fine-grained manner. We also use the entropy to guide semantic key generation to safeguard communications over open wireless channels. By doing so, both transmission efficiency and channel security can be simultaneously improved. Extensive experiments over various benchmarks show the effectiveness of the proposed SemEntropy. We discuss the reason why our proposed method benefits secure transmission of DLSC, and also give some interesting findings, e.g., SemEntropy can keep the semantic accuracy remain 95\% with 60\% less transmission.

翻译：近年来蓬勃发展的基于深度学习的语义通信（DLSC）聚焦于如何通过传输符号高效地向目的地传递期望语义。然而，神经模型的敏感性与无线信道的开放性导致DLSC系统极易遭受各类恶意攻击。这促使我们提出疑问：能否在发挥无线语义通信传输效率优势的同时，缓解其安全缺陷？基于此，我们提出SemEntropy——一种通过挖掘数据语义实现自适应传输与物理层加密的新方法。具体而言，我们首先引入语义熵，该指标表征DLSC传输目标下各类语义得分的期望。借助语义熵，我们能够以细粒度方式将高信息量语义动态映射至信道条件更优的正交频分复用（OFDM）子载波。同时，我们利用该熵值指导语义密钥生成，以保障开放无线信道中的通信安全。通过上述设计，传输效率与信道安全性得以同步提升。在多个基准数据集上的大量实验验证了所提SemEntropy的有效性。我们进一步探讨了该方法增强DLSC安全传输的机理，并揭示若干有趣现象，例如SemEntropy可在降低60%传输量的情况下保持95%的语义准确率。