Release SimLBR aigc and genimage heads

README.md

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+---
+license: mit
+library_name: pytorch
+pipeline_tag: image-classification
+tags:
+  - ai-generated-image-detection
+  - deepfake-detection
+  - dinov3
+  - cvpr2026
+---
+
+# SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images (CVPR 2026)
+
+[![arXiv](https://img.shields.io/badge/arXiv-2602.20412-b31b1b.svg?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2602.20412)
+[![GitHub](https://img.shields.io/badge/GitHub-mvrl%2FSimLBR-181717?logo=github)](https://github.com/mvrl/SimLBR)
+
+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](https://ai.meta.com/resources/models-and-libraries/dinov3-downloads/).
+
+## 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](https://github.com/mvrl/SimLBR) and set `DINO_V3_KEY` to your DINOv3 ViT-L/16 download URL from Meta:
+
+```bash
+git clone https://github.com/mvrl/SimLBR && cd SimLBR
+export DINO_V3_KEY=YOUR_DINOV3_VITL16_URL
+```
+
+Score images:
+
+```bash
+python -m simlbr.predict --pretrained aigc image1.png image2.jpg
+```
+
+Load in Python:
+
+```python
+from simlbr import SimLBR
+
+model = SimLBR.from_pretrained("aigc")  # or "genimage"
+```
+
+Reproduce benchmark numbers:
+
+```bash
+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
+
+```bibtex
+@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}
+}
+```

aigc/config.json

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+{
+  "backbone": "dinov3",
+  "activation": "relu",
+  "hidden_layers": 2,
+  "dropout": 0.3,
+  "lbr": true,
+  "lbrdist": [
+    0.5,
+    0.8
+  ]
+}

aigc/model.safetensors

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+oid sha256:0fc5cf6d30cb324ef651bdea4461ddd567569a16a89fca038b8ec839d8f21494
+size 2625988

genimage/config.json

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+{
+  "backbone": "dinov3",
+  "activation": "relu",
+  "hidden_layers": 2,
+  "dropout": 0.3,
+  "lbr": true,
+  "lbrdist": [
+    0.5,
+    0.8
+  ]
+}

genimage/model.safetensors

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+size 2625988