Recently proliferated semantic communications (SC) aim at effectively transmitting the semantics conveyed by the source and accurately interpreting the meaning at the destination. While such a paradigm holds the promise of making wireless communications more intelligent, it also suffers from severe semantic security issues, such as eavesdropping, privacy leaking, and spoofing, due to the open nature of wireless channels and the fragility of neural modules. Previous works focus more on the robustness of SC via offline adversarial training of the whole system, while online semantic protection, a more practical setting in the real world, is still largely under-explored. To this end, we present SemProtector, a unified framework that aims to secure an online SC system with three hot-pluggable semantic protection modules. Specifically, these protection modules are able to encrypt semantics to be transmitted by an encryption method, mitigate privacy risks from wireless channels by a perturbation mechanism, and calibrate distorted semantics at the destination by a semantic signature generation method. Our framework enables an existing online SC system to dynamically assemble the above three pluggable modules to meet customized semantic protection requirements, facilitating the practical deployment in real-world SC systems. Experiments on two public datasets show the effectiveness of our proposed SemProtector, offering some insights of how we reach the goal of secrecy, privacy and integrity of an SC system. Finally, we discuss some future directions for the semantic protection.

翻译：近年来蓬勃发展的语义通信（SC）旨在有效传输信源所传达的语义，并在目的地准确解析其含义。尽管这种范式有望使无线通信更加智能化，但由于无线信道的开放性和神经模块的脆弱性，它也面临着严重的语义安全问题，例如窃听、隐私泄露和欺骗。以往的研究更多通过离线对抗训练整个系统来增强SC的鲁棒性，而在线语义保护——这种在实际场景中更实用的设置——仍未得到充分探索。为此，我们提出了SemProtector，这是一个统一框架，旨在通过三个即插即用的语义保护模块来保障在线SC系统的安全性。具体而言，这些保护模块能够通过加密方法对传输语义进行加密，通过扰动机制降低来自无线信道的隐私风险，并通过语义签名生成方法校准目的地处的失真语义。我们的框架使现有在线SC系统能够动态组合上述三个即插模块，以满足定制化的语义保护需求，从而促进在实际SC系统中的部署。在两个公开数据集上的实验表明了所提SemProtector的有效性，并提供了关于如何实现SC系统保密性、隐私性和完整性的见解。最后，我们讨论了语义保护的一些未来方向。