README.md

metadata

license: apache-2.0
language:
  - en
pipeline_tag: image-segmentation
tags:
  - manipulation
  - forgery
  - image
  - cnn
  - transformer
  - residual-noise
  - efficientnet
  - swin
  - localization

NGIML Model Card

Inference

NGIML performs single-image forgery localization from a pretrained checkpoint and an input RGB image.

Checkpoints

Pretrained checkpoints are hosted on Hugging Face:

juhenes/ngiml

Available checkpoints:

• casia-effnet.pt
• casia-effnet+noise.pt
• casia-effnet+swin.pt
• casia-full.pt
• casia-swin.pt
• casia-swin+noise.pt

Run Inference

Recommended: Google Colab

The easiest way to test the model is through Colab:

• Google Colab: Open infer.ipynb in Colab

This is the recommended path for quick testing because the notebook is already set up for checkpoint-based inference.

Local CLI

If you want to run the project locally, use the repository files here:

• GitHub: juhenes/ngiml-infer

Install the dependencies:

pip install -r requirements.txt

Run the CLI:

python predict.py --checkpoint /path/to/checkpoint.pt --image /path/to/image.png

Example:

python predict.py --checkpoint checkpoints_cache/casia-full.pt --image /path/to/image.png

If --output-dir is omitted, outputs are saved under outputs/<image-stem>/.

Optional Arguments

• --output-dir to choose where outputs are saved
• --threshold to override the default binary threshold
• --normalization-mode to set imagenet or zero_one
• --resize-max-side to resize large images before preprocessing
• --crop-size to override the inference crop size
• --device to choose a device such as cpu or cuda:0

Output Files

When an output directory is used, the runtime saves:

• input_rgb.png
• preview_input_rgb.png
• preview_probability_map.png
• preview_binary_mask.png
• preview_overlay.png
• probability_map.png
• binary_mask.png
• overlay.png
• prediction.json

prediction.json includes summary metadata such as the checkpoint path, threshold, normalization mode, device, and basic prediction statistics.

References

1. Dong, J., Wang, W., and Tan, T. "CASIA Image Tampering Detection Evaluation Database." 2013 IEEE China Summit and International Conference on Signal and Information Processing, 2013. DOI

@inproceedings{Dong2013,
  doi = {10.1109/chinasip.2013.6625374},
  url = {https://doi.org/10.1109/chinasip.2013.6625374},
  year = {2013},
  month = jul,
  publisher = {{IEEE}},
  author = {Jing Dong and Wei Wang and Tieniu Tan},
  title = {{CASIA} Image Tampering Detection Evaluation Database},
  booktitle = {2013 {IEEE} China Summit and International Conference on Signal and Information Processing}
}

2. Pham, N. T., Lee, J.-W., Kwon, G.-R., and Park, C.-S. "Hybrid Image-Retrieval Method for Image-Splicing Validation." Symmetry, 11(1), 83, 2019.

@article{pham2019hybrid,
  title = {Hybrid Image-Retrieval Method for Image-Splicing Validation},
  author = {Pham, Nam Thanh and Lee, Jong-Weon and Kwon, Goo-Rak and Park, Chun-Su},
  journal = {Symmetry},
  volume = {11},
  number = {1},
  pages = {83},
  year = {2019},
  publisher = {Multidisciplinary Digital Publishing Institute}
}