The field of semantic communication and diffusion models is rapidly advancing, with a focus on improving the efficiency and robustness of image and video transmission. Recent developments have introduced new frameworks and techniques, such as multi-hop parallel image semantic communication and decoupled diffusion multi-frame compensation, which aim to mitigate distortion accumulation and reduce communication overhead. Additionally, there is a growing interest in applying diffusion models to various applications, including concept erasure, generative semantic coding, and secure distributed consensus estimation. Noteworthy papers in this area include: Multi-hop Parallel Image Semantic Communication for Distortion Accumulation Mitigation, which proposes a novel framework for robust multi-hop image transmission. Rethinking Robust Adversarial Concept Erasure in Diffusion Models, which introduces a semantics-guided approach for effective concept erasure. FreeSliders: Training-Free, Modality-Agnostic Concept Sliders for Fine-Grained Diffusion Control, which enables plug-and-play concept control across modalities. SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding, which enhances the security and robustness of deep joint source-channel coding under adversarial wireless environments.