Present and Future Generalization of Synthetic Image Detectors
Authors: name: Pablo Bernabeu-Perez, name: Enrique Lopez-Cuena, name: Dario Garcia-Gasulla

The continued release of new and better image generation models increases the
demand for synthetic image detectors. In such a dynamic field, detectors need
to be able to generalize widely and be robust to uncontrolled alterations. The
present work is motivated by this setting, when looking at the role of time,
image transformations and data sources, for detector generalization. In these
experiments, none of the evaluated detectors is found universal, but results
indicate an ensemble could be. Experiments on data collected in the wild show
this task to be more challenging than the one defined by large-scale datasets,
pointing to a gap between experimentation and actual practice. Finally, we
observe a race equilibrium effect, where better generators lead to better
detectors, and vice versa. We hypothesize this pushes the field towards a
perpetually close race between generators and detectors.