  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.
