Title: Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts Show Chinese title

Title: 通过多模态提示的生成式AI辅助联合无训练的安全语义通信

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. Show Chinese abstract

Abstract: 语义通信（SemCom）有望在实现通信目标的同时减少网络资源消耗，然而，联合训练语义编码器和解码器以及随后在网络设备中的部署所带来的计算开销却被忽视了。生成式人工智能（GAI）的最新进展提供了一个潜在的解决方案。GAI模型的强大学习能力表明，语义解码器可以利用有限的语义信息（如提示）重建源消息，而无需与语义编码器联合训练。然而，一个显著的挑战是GAI多样化生成能力引入的不稳定性。这种不稳定性，在文本生成图像等输出中显而易见，限制了GAI在需要准确消息恢复的场景中的直接应用，例如人脸图像传输。 为了解决上述问题，本文提出了一种基于GAI的多模态提示语义通信系统，以实现准确的内容解码。此外，针对安全方面的担忧，我们介绍了由友好干扰机辅助的隐蔽通信的应用。该系统借助生成扩散模型，共同优化扩散步骤、干扰和发射功率，从而实现源消息的成功和安全传输。