FakeInversion: Learning to Detect Images from Unseen Text-to-Image Models by Inverting Stable Diffusion

George Cazenavette, Avneesh Sud, Thomas Leung, Ben Usman

Abstract: Due to the high potential for abuse of GenAl systems, the task of detecting synthetic images has recently become of great interest to the research community. Unfortunately, ex-isting 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 se-tups. 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.