Present and Future Generalization of Synthetic Image Detectors

The chapter delves into the critical challenges and advancements in synthetic image detection (SID), highlighting the ongoing race between synthetic image generation (SIG) and detection technologies. It systematically analyzes how different training conditions affect the generalization capacity of SID models, emphasizing the importance of robust and adaptable detection methods. The development and release of SuSy, a multi-class detector trained with optimized augmentations, is a significant contribution, accompanied by practical deployment guidelines. The chapter also provides comprehensive benchmarking of state-of-the-art detectors across diverse datasets, identifying critical vulnerabilities related to image rescaling and other common alterations. Ethical considerations related to SID research, including the responsible release of detectors and the impact of open science, are thoroughly discussed. The findings underscore the need for continuous improvement in SID technologies to keep pace with advancements in synthetic image generation, ensuring reliable detection and maintaining trust in digital environments.