Generative AI-aided Joint Training-free Secure Semantic Communications
  via Multi-modal Prompts
Hongyang Du Guangyuan Liu Dusit Niyato Jiayi Zhang Jiawen Kang Zehui Xiong Bo Ai Dong In Kim
Semantic communication (SemCom) holds promise for reducing network resource
consumption while achieving the communications goal. However, the computational
overheads in jointly training semantic encoders and decoders-and the subsequent
deployment in network devices-are overlooked. Recent advances in Generative
artificial intelligence (GAI) offer a potential solution. The robust learning
abilities of GAI models indicate that semantic decoders can reconstruct source
messages using a limited amount of semantic information, e.g., prompts, without
joint training with the semantic encoder. A notable challenge, however, is the
instability introduced by GAI's diverse generation ability. This instability,
evident in outputs like text-generated images, limits the direct application of
GAI in scenarios demanding accurate message recovery, such as face image
transmission. To solve the above problems, this paper proposes a GAI-aided
SemCom system with multi-model prompts for accurate content decoding. Moreover,
in response to security concerns, we introduce the application of covert
communications aided by a friendly jammer. The system jointly optimizes the
diffusion step, jamming, and transmitting power with the aid of the generative
diffusion models, enabling successful and secure transmission of the source
messages.