SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images (CVPR 2026)

Pretrained AI-generated image detectors from the CVPR 2026 paper SimLBR. A frozen DINOv3 ViT-L/16 backbone (LVD-1689M weights) produces one CLS embedding per image, and a small MLP head, trained with Latent Blending Regularization (LBR), predicts whether the image is real or fake.

Each file in this repo holds only the MLP head (~2.6 MB). The DINOv3 backbone is not redistributed here; it is downloaded from Meta under the DINOv3 license.

Models

Variant	Files	Training data	Benchmark	Avg. Acc	Avg. AP
aigc	aigc/	ProGAN	AIGCDetectionBenchMark (16 subsets)	88.4	96.1
genimage	genimage/	GenImage SD v1.4	GenImage (8 subsets)	91.1	97.1

Note: the GenImage numbers above differ from the paper. The paper's GenImage ADM entry was evaluated on real images only, because a data-loading bug skipped ADM's fake images (.PNG extension). The numbers here include all ADM images and are what the released code reproduces.

Head: Linear(1024→512) → ReLU → Dropout → Linear(512→256) → ReLU → Dropout → Linear(256→1), trained with LBR α ~ U(0.5, 0.8). Output is a logit; sigmoid(logit) > 0.5 means fake.

Usage

Install the SimLBR code and set DINO_V3_KEY to your DINOv3 ViT-L/16 download URL from Meta:

git clone https://github.com/mvrl/SimLBR && cd SimLBR
export DINO_V3_KEY=YOUR_DINOV3_VITL16_URL

Score images:

python -m simlbr.predict --pretrained aigc image1.png image2.jpg

Load in Python:

from simlbr import SimLBR

model = SimLBR.from_pretrained("aigc")  # or "genimage"

Reproduce benchmark numbers:

python -m simlbr.evaluate --dataset_name aigc --data_dir /path/to/AIGCDetectionBenchMark --pretrained aigc

Preprocessing matters: images must be resized to 256×256 and normalized with mean=(0.430, 0.411, 0.296), std=(0.213, 0.156, 0.143), as in simlbr.utils.make_dinov3_transform(). The heads were trained with these exact statistics.

Citation

@article{dhakal2026simlbr,
  title={SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images},
  author={Dhakal, Aayush and Khanal, Subash and Sastry, Srikumar and Arndt, Jacob and Ambrozio Dias, Philipe and Lunga, Dalton and Jacobs, Nathan},
  booktitle={Computer Vision and Pattern Recognition},
  year={2026},
  organization={IEEE/CVF}
}