The rise of diffusion models has significantly improved the fidelity and diversity of generated images. With numerous benefits, these advancements also introduce new risks. Diffusion models can be exploited to create high-quality Deepfake images, which poses challenges for image authenticity verification. In recent years, research on generalizable diffusion-generated image detection has grown rapidly. However, a comprehensive review of this topic is still lacking. To bridge this gap, we present a systematic survey of recent advances and classify them into two main categories: (1) data-driven detection and (2) feature-driven detection. Existing detection methods are further classified into six fine-grained categories based on their underlying principles. Finally, we identify several open challenges and envision some future directions, with the hope of inspiring more research work on this important topic. Reviewed works in this survey can be found atthis https URL.
View on arXiv@article{xu2025_2502.19716,
title={ Recent Advances on Generalizable Diffusion-generated Image Detection },
author={ Qijie Xu and Defang Chen and Jiawei Chen and Siwei Lyu and Can Wang },
journal={arXiv preprint arXiv:2502.19716},
year={ 2025 }
}