The field of semantic communications is moving towards developing innovative frameworks that prioritize the transmission of meaningful information over raw bit data. This shift is driven by the need for more efficient and robust communication systems, particularly in resource-constrained or noisy environments. Recent research has focused on leveraging diffusion models and artificial intelligence to enhance the security and adaptability of semantic communication systems. Notably, the use of diffusion-based frameworks has shown promise in securing semantic transmission against eavesdropping and jamming attacks. Additionally, task-adaptive semantic communication frameworks have been proposed to dynamically adjust the semantic message delivery according to various downstream tasks.

Noteworthy papers include:

• Towards Secure Semantic Transmission In the Era of GenAI: A Diffusion-based Framework, which proposes a novel framework for securing semantic transmission using diffusion models.
• Task-Adaptive Semantic Communications with Controllable Diffusion-based Data Regeneration, which presents a framework for adaptively conveying critical semantic information according to various downstream tasks.
• Joint Source-Channel Noise Adding with Adaptive Denoising for Diffusion-Based Semantic Communications, which introduces a novel approach to transforming harmful channel noise into a constructive component of the diffusion-based semantic reconstruction process.