| --- |
| base_model: facebook/dinov3-vitl16-pretrain-lvd1689m |
| datasets: |
| - Jason37437/Doc-Protocol-Data |
| language: |
| - en |
| library_name: pytorch |
| license: mit |
| metrics: |
| - f1 |
| pipeline_tag: image-segmentation |
| tags: |
| - image-forgery-localization |
| - document-forensics |
| - computer-vision |
| --- |
| |
| # RiSE |
|
|
| Checkpoints for **RiSE: Residual Subspace Expert for Generalizable Text-Centric Image Forgery Localization**. |
|
|
| Paper: [RiSE: Residual Subspace Expert for Generalizable Text-Centric Image Forgery Localization](https://huggingface.co/papers/2610.24480) |
|
|
| Code and usage: https://github.com/KahimWong/RiSE |
|
|
| | Model | Checkpoint | Configuration | |
| |---|---|---| |
| | RiSE-1 | [RiSE-1.pth](RiSE-1.pth) | [RiSE-1.json](RiSE-1.json) | |
| | RiSE-3 | [RiSE-3.pth](RiSE-3.pth) | [RiSE-3.json](RiSE-3.json) | |
| | RiSE-9 | [RiSE-9.pth](RiSE-9.pth) | [RiSE-9.json](RiSE-9.json) | |
| | RiSE-9-DCT | [RiSE-9-DCT.pth](RiSE-9-DCT.pth) | [RiSE-9-DCT.json](RiSE-9-DCT.json) | |
|
|
| The original checkpoint bytes are preserved; only public filenames are simplified. SHA-256 hashes and sizes are in `checksums.json`. Load these trusted PyTorch checkpoints using the strict loader in the code repository. |
|
|
| All models use DINOv3 ViT-L/16. RiSE-9-DCT uses the 1024-channel DCT projector and fusion at zero-based block index 10. No legacy DCT checkpoints are included. Inference accepts 512x512 images; DCT inference requires JPEG input. Pretrained-backbone terms apply separately. |
|
|
| ## Single-Image Inference |
|
|
| Install the [RiSE code](https://github.com/KahimWong/RiSE) and download a checkpoint with its matching config: |
|
|
| ```bash |
| git clone https://github.com/KahimWong/RiSE.git |
| cd RiSE |
| pip install torch==2.4.0 torchvision==0.19.0 --index-url https://download.pytorch.org/whl/cu121 |
| pip install -r requirements.txt |
| |
| hf download Jason37437/RiSE RiSE-9.pth RiSE-9.json --local-dir checkpoints |
| python infer.py \ |
| --checkpoint checkpoints/RiSE-9.pth --config checkpoints/RiSE-9.json \ |
| --image /path/to/image.jpg --output outputs/demo --gh-order 5 |
| ``` |
|
|
| ## Doc Protocol Benchmark |
|
|
| Train on DocTamper and evaluate on seven test sets. In-domain FCD/SCD/Test images are compressed exactly once using the official final quality-factor record (`record[-1]`). Cross-domain T-SROIE/OSTF/TPIC-13/RTM images are original RGB JPEG patches without additional resizing or compression. |
|
|
|  |
|
|
| ## Seven-Set Evaluation |
|
|
| After preparing `configs/data.json` as described in the [code README](https://github.com/KahimWong/RiSE#-data-preparation), run: |
|
|
| ```bash |
| python evaluate.py \ |
| --checkpoint checkpoints/RiSE-9.pth --config checkpoints/RiSE-9.json \ |
| --data configs/data.json --output outputs/rise9_eval --gh-order 5 |
| ``` |
