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

TL;DR

This paper proposes a secure semantic communication system leveraging generative AI and multi-modal prompts to reduce training overhead, improve decoding accuracy, and enhance security through covert communication techniques.

Contribution

It introduces a novel GAI-aided SemCom framework with multi-model prompts and a secure transmission scheme using friendly jammers, addressing training complexity and security issues.

Findings

Effective content decoding with limited semantic prompts

Enhanced security via covert communication with jamming

Stable message reconstruction despite GAI output variability

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.