The field of image processing and generation is rapidly evolving, with a focus on developing more robust and generalizable models. Recent research has emphasized the importance of evaluating models under real-world conditions, where they are exposed to diverse and challenging scenarios. This has led to the creation of new benchmarks and datasets, such as the Real-World Robustness Dataset, which assess the performance of models in detecting AI-generated images and other tasks. Notably, the use of diffusion models has shown great promise in generating high-quality images and improving the robustness of detectors. Furthermore, multi-agent frameworks and large language models have been employed to simulate realistic scenarios and generate diverse datasets, addressing the issue of data scarcity and improving model performance. Some noteworthy papers in this area include: Bridging the Gap Between Ideal and Real-world Evaluation, which introduces the Real-World Robustness Dataset for evaluating AI-generated image detection models. GAMMA, which proposes a novel training framework for AI-generated image detection that enhances semantic alignment and reduces domain bias. Double Helix Diffusion, which presents a cross-domain generative framework for synthesizing anomaly images and their pixel-level annotation masks. Agent4FaceForgery, which introduces a multi-agent framework for simulating face forgery creation and generating diverse datasets. End4, which proposes a novel detection method based on end-to-end denoising diffusion for identifying images generated by diffusion-based inpainting models.