  Semantic communications have been envisioned as a potential technique that
goes beyond Shannon paradigm. Unlike modern communications that provide
bit-level security, the eaves-dropping of semantic communications poses a
significant risk of potentially exposing intention of legitimate user. To
address this challenge, a novel deep neural network (DNN) enabled secure
semantic communication (DeepSSC) system is developed by capitalizing on
physical layer security. To balance the tradeoff between security and
reliability, a two-phase training method for DNNs is devised. Particularly,
Phase I aims at semantic recovery of legitimate user, while Phase II attempts
to minimize the leakage of semantic information to eavesdroppers. The loss
functions of DeepSSC in Phases I and II are respectively designed according to
Shannon capacity and secure channel capacity, which are approximated with
variational inference. Moreover, we define the metric of secure bilingual
evaluation understudy (S-BLEU) to assess the security of semantic
communications. Finally, simulation results demonstrate that DeepSSC achieves a
significant boost to semantic security particularly in high signal-to-noise
ratio regime, despite a minor degradation of reliability.
