  New advancements for the detection of synthetic images are critical for
fighting disinformation, as the capabilities of generative AI models
continuously evolve and can lead to hyper-realistic synthetic imagery at
unprecedented scale and speed. In this paper, we focus on the challenge of
generalizing across different concept classes, e.g., when training a detector
on human faces and testing on synthetic animal images - highlighting the
ineffectiveness of existing approaches that randomly sample generated images to
train their models. By contrast, we propose an approach based on the premise
that the robustness of the detector can be enhanced by training it on realistic
synthetic images that are selected based on their quality scores according to a
probabilistic quality estimation model. We demonstrate the effectiveness of the
proposed approach by conducting experiments with generated images from two
seminal architectures, StyleGAN2 and Latent Diffusion, and using three
different concepts for each, so as to measure the cross-concept generalization
ability. Our results show that our quality-based sampling method leads to
higher detection performance for nearly all concepts, improving the overall
effectiveness of the synthetic image detectors.
