The field of deepfake detection and AI-generated image forensics is rapidly evolving, with a growing focus on developing robust and trustworthy detection systems. Recent research has emphasized the importance of combining deep learning models with forensic analysis to improve detection accuracy and interpretability. Hybrid approaches have shown promise in achieving a balance between adaptability and interpretability, enabling the development of more resilient and reliable detection systems. Notable advancements include the use of reference-aware audiovisual deepfake detection methods and agent-based forensic frameworks that emulate human investigation. These innovations have the potential to significantly enhance the detection of AI-generated media and mitigate the risks associated with misinformation and digital trust. Noteworthy papers include: A Hybrid Deep Learning and Forensic Approach for Robust Deepfake Detection, which proposed a hybrid framework that consistently outperformed single-method baselines and achieved state-of-the-art performance. Referee: Reference-aware Audiovisual Deepfake Detection, which introduced a novel reference-aware audiovisual deepfake detection method that achieved state-of-the-art performance on cross-dataset and cross-language evaluation protocols. From Evidence to Verdict: An Agent-Based Forensic Framework for AI-Generated Image Detection, which introduced a novel training-free framework that employed a set of forensic tools and achieved 97.05% accuracy, substantially outperforming traditional classifiers and state-of-the-art VLMs.