The field of synthetic image detection and generation is rapidly evolving, with a focus on improving the effectiveness of synthetic image detectors (SIDs) and generating high-quality synthetic images for various applications. Recent developments have led to the creation of innovative methods for red teaming, which involves identifying and exploiting the failure modes of SIDs to improve their performance. Additionally, there is a growing interest in generating synthetic images that are tailored to specific applications, such as surgical image synthesis, to overcome the scarcity of annotated data. Noteworthy papers in this area include: PolyJuice Makes It Real: Black-Box, Universal Red Teaming for Synthetic Image Detectors, which proposes a black-box, image-agnostic red-teaming method for SIDs. Towards Application Aligned Synthetic Surgical Image Synthesis, which introduces a framework for aligning diffusion models with downstream objectives to generate high-quality synthetic surgical images.