The field of image forensics and synthetic media detection is rapidly evolving, with a focus on developing more accurate and generalizable methods for detecting AI-generated images and identifying their sources. Recent research has highlighted the importance of modeling forensic microstructures, such as subtle pixel-level patterns unique to the image creation process, to improve detection and attribution capabilities. The use of vision-language models and contrastive learning techniques has also shown promise in this area. Furthermore, the development of new datasets and benchmarks, such as those focused on open-world diffusion-based manipulations, is driving innovation and advancement in the field. Notably, papers such as CO-SPY and FakeReasoning have introduced novel frameworks for detecting synthetic images and providing accurate detection through structured reasoning over forgery attributes. Additionally, the work presented in OpenSDI and Forensic Self-Descriptions Are All You Need has demonstrated significant improvements in spotting diffusion-generated images and performing zero-shot detection of synthetic images. Overall, the field is moving towards more robust and generalizable methods for image forensics and synthetic media detection, with potential applications in areas such as authenticity verification and copyright protection. Noteworthy papers include: CO-SPY, which achieved an average accuracy improvement of approximately 11% to 34% over existing methods, and FakeReasoning, which demonstrated robust generalization and outperformed state-of-the-art methods on both detection and reasoning tasks.