  Due to the high potential for abuse of GenAI systems, the task of detecting
synthetic images has recently become of great interest to the research
community. Unfortunately, existing image-space detectors quickly become
obsolete as new high-fidelity text-to-image models are developed at blinding
speed. In this work, we propose a new synthetic image detector that uses
features obtained by inverting an open-source pre-trained Stable Diffusion
model. We show that these inversion features enable our detector to generalize
well to unseen generators of high visual fidelity (e.g., DALL-E 3) even when
the detector is trained only on lower fidelity fake images generated via Stable
Diffusion. This detector achieves new state-of-the-art across multiple training
and evaluation setups. Moreover, we introduce a new challenging evaluation
protocol that uses reverse image search to mitigate stylistic and thematic
biases in the detector evaluation. We show that the resulting evaluation scores
align well with detectors' in-the-wild performance, and release these datasets
as public benchmarks for future research.
