Detecting and Attributing Images Generated by State-of-the-Art Text-to-Image Diffusion Models

This study presents extensive analyses on detecting and attributing images generated by 12 state-of-the-art text-to-image diffusion models, including the ability to identify subtle variations in hyperparameters used during the inference stage and the impact of post-editing enhancements on attribution accuracy. The research also introduces a novel approach to uncover detectable traces across different levels of visual granularity, from high-frequency perturbations to mid-level representations.