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
Code and usage: https://github.com/KahimWong/RiSE
| Model | Checkpoint | Configuration |
|---|---|---|
| RiSE-1 | RiSE-1.pth | RiSE-1.json |
| RiSE-3 | RiSE-3.pth | RiSE-3.json |
| RiSE-9 | RiSE-9.pth | RiSE-9.json |
| RiSE-9-DCT | RiSE-9-DCT.pth | 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 and download a checkpoint with its matching config:
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, run:
python evaluate.py \
--checkpoint checkpoints/RiSE-9.pth --config checkpoints/RiSE-9.json \
--data configs/data.json --output outputs/rise9_eval --gh-order 5