AI-Generated Image Detectors Overrely on Global Artifacts: Evidence from Inpainting Exchange
Modern deep learning-based inpainting enables realistic local image manipulation, raising critical challenges for reliable detection. However, we observe that current detectors primarily rely on global artifacts that appear as inpainting side effects, rather than on locally synthesized content. We show that this behavior occurs because VAE-based reconstruction induces a subtle but pervasive spectral shift across the entire image, including unedited regions. To isolate this effect, we introduce Inpainting Exchange (INP-X), an operation that restores original pixels outside the edited region while preserving all synthesized content. We create a 90K test dataset including real, inpainted, and exchanged images to evaluate this phenomenon. Under this intervention, pretrained state-of-the-art detectors, including commercial ones, exhibit a dramatic drop in accuracy (e.g., from 91\% to 55\%), frequently approaching chance level. We provide a theoretical analysis linking this behavior to high-frequency attenuation caused by VAE information bottlenecks. Our findings highlight the need for content-aware detection. Indeed, training on our dataset yields better generalization and localization than standard inpainting. Our dataset and code are publicly available at https://github.com/emirhanbilgic/INP-X.
6981cd9a47987be58cdb7c65
Elif Nebioglu
6981cd9a47987be58cdb7c66
Emirhan Bilgiç
claimed_verified
2026-02-03T13:58:08.337Z
64a3d8d3de901eb01ded1b2c
/avatars/1dab0720e2f937436668fdd61cf76ce5.svg
Emirhan Bilgiç
emirhanbilgic
user
emirhanbilgic
6981cd9a47987be58cdb7c67
Adrian Popescu
AI-Generated Image Detectors Overrely on Global Artifacts: Evidence from Inpainting Exchange
Modern deep learning-based inpainting enables realistic local image manipulation, raising critical challenges for reliable detection. However, we observe that current detectors primarily rely on global artifacts that appear as inpainting side effects, rather than on locally synthesized content. We show that this behavior occurs because VAE-based reconstruction induces a subtle but pervasive spectral shift across the entire image, including unedited regions. To isolate this effect, we introduce Inpainting Exchange (INP-X), an operation that restores original pixels outside the edited region while preserving all synthesized content. We create a 90K test dataset including real, inpainted, and exchanged images to evaluate this phenomenon. Under this intervention, pretrained state-of-the-art detectors, including commercial ones, exhibit a dramatic drop in accuracy (e.g., from 91\% to 55\%), frequently approaching chance level. We provide a theoretical analysis linking this behavior to high-frequency attenuation caused by VAE information bottlenecks. Our findings highlight the need for content-aware detection. Indeed, training on our dataset yields better generalization and localization than standard inpainting. Our dataset and code are publicly available at https://github.com/emirhanbilgic/INP-X.
VAE-based inpainting creates spectral shifts that fool detection systems, which can be mitigated through Inpainting Exchange to improve content-aware detection performance.
VAE-based reconstruction spectral shift inpainting detection systems Inpainting Exchange high-frequency attenuation information bottlenecks content-aware detection