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：近年来兴起的基于深度学习的语义通信（DLSC）主要关注如何使传输符号高效地向接收端传递预期含义。然而，神经模型的敏感性与无线信道的开放性使得DLSC系统极易受到各类恶意攻击。这促使我们思考一个问题："能否在利用无线语义通信传输效率优势的同时，缓解其安全性缺陷？"。基于此，我们提出SemEntropy这一新方法，通过挖掘数据的语义特性以同时实现自适应传输与物理层加密，从而对上述问题作出肯定回答。具体而言，我们首先提出语义熵概念，其表征了针对DLSC传输目标的各种语义得分的期望值。借助该语义熵，我们能够以细粒度方式将信息量丰富的语义动态分配给信道条件更优的正交频分复用（OFDM）子载波。同时，我们利用该熵值引导语义密钥生成，以保障开放无线信道上的通信安全。通过这种方式，传输效率与信道安全性得以同步提升。在多基准测试上的大量实验验证了所提SemEntropy的有效性。我们深入探讨了该方法有益于DLSC安全传输的内在机理，并给出若干重要发现，例如SemEntropy可在减少60%传输量的情况下保持95%的语义准确率。