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Semantic communication (SemCom) holds promise for reducing network resourceconsumption while achieving the communications goal. However, the computationaloverheads in jointly training semantic encoders and decoders-and the subsequentdeployment in network devices-are overlooked. Recent advances in Generativeartificial intelligence (GAI) offer a potential solution. The robust learningabilities of GAI models indicate that semantic decoders can reconstruct sourcemessages using a limited amount of semantic information, e.g., prompts, withoutjoint training with the semantic encoder. A notable challenge, however, is theinstability introduced by GAI's diverse generation ability. This instability,evident in outputs like text-generated images, limits the direct application ofGAI in scenarios demanding accurate message recovery, such as face imagetransmission. To solve the above problems, this paper proposes a GAI-aidedSemCom system with multi-model prompts for accurate content decoding. Moreover,in response to security concerns, we introduce the application of covertcommunications aided by a friendly jammer. The system jointly optimizes thediffusion step, jamming, and transmitting power with the aid of the generativediffusion models, enabling successful and secure transmission of the sourcemessages.

Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts