The field of synthetic media and forensic analysis is rapidly evolving, with a focus on developing robust tools for detecting and attributing synthetic content. Recent developments have centered around creating high-fidelity synthetic datasets, such as those designed for obstacle detection in railway environments and realistic image generation. These datasets have the potential to advance safety applications and support the forensic community in developing detection and attribution techniques. Furthermore, research has highlighted the importance of integrating spectral transforms, color distribution metrics, and local feature descriptors to extract discriminative statistical signatures embedded in synthetic outputs. This has significant implications for copyright enforcement, privacy protection, and legal compliance. Noteworthy papers in this area include:

• SynRailObs, a synthetic dataset for obstacle detection in railway scenarios, which demonstrates substantial potential for advancing railway safety applications.
• Deepfake Forensic Analysis, a novel forensic framework for identifying the training dataset of GAN-generated images, which achieves high accuracy in dataset attribution and has significant implications for legal and ethical considerations.
• DRAGON, a large-scale dataset of realistic images generated by diffusion models, which supports the development and evaluation of detection and attribution techniques for synthetic content.
• Accuracy and Fairness of Facial Recognition Technology in Low-Quality Police Images, a study that highlights concerns about fairness and reliability when facial recognition technology is used in real-world investigative contexts.