Generative AI-aided Joint Training-free Secure Semantic Communications
via Multi-modal Prompts
- URL: http://arxiv.org/abs/2309.02616v1
- Date: Tue, 5 Sep 2023 23:24:56 GMT
- Title: Generative AI-aided Joint Training-free Secure Semantic Communications
via Multi-modal Prompts
- Authors: Hongyang Du, Guangyuan Liu, Dusit Niyato, Jiayi Zhang, Jiawen Kang,
Zehui Xiong, Bo Ai, and Dong In Kim
- Abstract summary: This paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding.
In response to security concerns, we introduce the application of covert communications aided by a friendly jammer.
- Score: 89.04751776308656
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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