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Recently proliferated semantic communications (SC) aim at effectivelytransmitting the semantics conveyed by the source and accurately interpretingthe meaning at the destination. While such a paradigm holds the promise ofmaking wireless communications more intelligent, it also suffers from severesemantic security issues, such as eavesdropping, privacy leaking, and spoofing,due to the open nature of wireless channels and the fragility of neuralmodules. Previous works focus more on the robustness of SC via offlineadversarial training of the whole system, while online semantic protection, amore practical setting in the real world, is still largely under-explored. Tothis end, we present SemProtector, a unified framework that aims to secure anonline SC system with three hot-pluggable semantic protection modules.Specifically, these protection modules are able to encrypt semantics to betransmitted by an encryption method, mitigate privacy risks from wirelesschannels by a perturbation mechanism, and calibrate distorted semantics at thedestination by a semantic signature generation method. Our framework enables anexisting online SC system to dynamically assemble the above three pluggablemodules to meet customized semantic protection requirements, facilitating thepractical deployment in real-world SC systems. Experiments on two publicdatasets show the effectiveness of our proposed SemProtector, offering someinsights of how we reach the goal of secrecy, privacy and integrity of an SCsystem. Finally, we discuss some future directions for the semantic protection.

SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems