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

arXiv: https://arxiv.org/abs/2309.02616 · HTML: https://mlsanity.com/paper/2309.02616/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 17 references that are in the map.
- First posted on arXiv 5 Sep 2023.

## Builds on (most cited first, 16 of 16)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Diffusion Autoencoders: Toward a Meaningful and Decodable Representation](https://mlsanity.com/paper/2111.15640/) (2021)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://mlsanity.com/paper/2109.06977/) (2021)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023)
- [From Semantic Communication to Semantic-aware Networking: Model, Architecture, and Open Problems](https://mlsanity.com/paper/2012.15405/) (2020)
- [Exploring Collaborative Distributed Diffusion-Based AI-Generated Content (AIGC) in Wireless Networks](https://mlsanity.com/paper/2304.03446/) (2023)
- [AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing](https://mlsanity.com/paper/2303.01896/) (2023)
- [Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation](https://mlsanity.com/paper/2308.04942/) (2023)

## Built on by (most cited first, 7 of 7)

- [Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges](https://mlsanity.com/paper/2405.17454/) (2024)
- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey](https://mlsanity.com/paper/2401.13699/) (2024)
- [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025)
- [Visual Fidelity Index for Generative Semantic Communications with Critical Information Embedding](https://mlsanity.com/paper/2505.10405/) (2025)
- [IRS-Enhanced Secure Semantic Communication Networks: Cross-Layer and Context-Awared Resource Allocation](https://mlsanity.com/paper/2411.01821/) (2024)
- [Optimization of Private Semantic Communication Performance: An Uncooperative Covert Communication Method](https://mlsanity.com/paper/2508.07586/) (2025)

## Abstract (arXiv)

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.