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.
@article{arxiv.2309.02616,
title = {Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts},
author = {Hongyang Du and Guangyuan Liu and Dusit Niyato and Jiayi Zhang and Jiawen Kang and Zehui Xiong and Bo Ai and Dong In Kim},
journal= {arXiv preprint arXiv:2309.02616},
year = {2023}
}