Model weights for DEAR ("Dissect and Prune: Enhancing Robustness in AI-Generated Image Detection", ICML 2026). DEAR is a post-hoc feature-selection method that dissects a pretrained detector's channels by Regional Activation Discrepancy, bilaterally prunes the extremes, and refines the linear classifier.

corvi/model_best.pth     # base Corvi detector (ResNet-50)
rajan/model_best.pth     # base Rajan detector (ResNet-50, AlignedForensics)
dear_c/model_best.pth    # DEAR-c (ours, Corvi base)
dear_r/model_best.pth    # DEAR-r (ours, Rajan base)

See the code repo for the full inference and evaluation pipeline.

The base detectors and DEAR models were trained on LSUN and MS COCO real images, LDM fake images from the AlignedForensics training data (originally the DMimageDetection latent diffusion set by GRIP-UNINA, Corvi et al.), the AlignedForensics aligned pairs (Rajan), and the SD 1.5 inpainted diagnostic set (DEAR refinement). The backbone is a ResNet-50 from GRIP-UNINA initialized from torchvision ImageNet weights.

Released under CC BY-NC 4.0 for research and non-commercial use. Because the DEAR models are refined on Stable Diffusion 1.5 generated data, they also carry the use-based restrictions of the CreativeML OpenRAIL-M license. See NOTICE and LICENSE for the full attribution and terms.

For the DEAR models (dear_c, dear_r), cite the DEAR paper.

For the base detectors, cite the original works. The Corvi detector (corvi) and its training data are from Corvi et al. The Rajan detector (rajan) is from Sundara Rajan et al. (AlignedForensics).