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[NeurIPS'24 Spotlight] A comprehensive benchmark & codebase for Image manipulation detection/localization.

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[NeurIPS'24 Spotlight] IMDL-BenCo: Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization

Xiaochen Ma †, Xuekang Zhu†, Lei Su†, Bo Du†, Zhuohang Jiang†, Bingkui Tong†, Zeyu Lei†, Xinyu Yang†, Chi-Man Pun, Jiancheng Lv, Jizhe Zhou *


†: joint first author & equal contribution *: corresponding author
🏎️Special thanks to Dr. Wentao Feng for the workplace, computation power, and physical infrastructure support.

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News

  • [2024/09/26] This paper, IMDL-BenCo, has been accepted as Spotlight to NeurIPS 2024 Track Datasets and Benchmarks!!! 🎉🎉🎉

Warning

Existing bug:

  1. The Image-Acc evaluator may not return an accurate value under the current version during multi-GPU inference. Please be careful with it! We are locating the exact issue. Details found in issue: #42. Thanks @iamwangyabin for reporting the issue.

Overview

☑️Welcome to IMDL-BenCo, the first comprehensive IMDL benchmark and modular codebase.

  • This codebase is under long-term maintenance and updating. New features, extra baseline/sota models, and bug fixes will be continuously involved. You can find the corresponding plan here shortly.
  • This repo decomposes the IMDL framework into standardized, reusable components and revises the model construction pipeline, improving coding efficiency and customization flexibility.
  • This repo fully implements or incorporates training code for state-of-the-art models to establish a comprehensive IMDL benchmark.
  • Cite and star if you feel helpful. This will encourage us a lot 🥰.

Features under developing

This repository has completed training, testing, robustness testing, Grad-CAM, and other functionalities for mainstream models.

However, more features are currently being tested for improved user experience. Updates will be rolled out frequently. Stay tuned!

  • Install and download via PyPI

    • You can experience on test PyPI now!
  • Based on command line invocation, similar to conda in Anaconda.

    • Dynamically create all training scripts to support personalized modifications.
  • Information library, downloading, and re-management of IMDL datasets.

  • Support for Weight & Bias visualization.

Quick Start

IMDL-BenCo is a Python library managed on PYPI now, it's easy to install by following the command:

pip install imdlbenco

For further guidance, please click the buttons below for official documentation:

Documents Documents

Documents Documents

We will keep updating the document with tricks and user cases. Please stay tuned!

We also welcome contributors to translate it into other languages.

About

☑️About the Developers:

Citation

If you find our work valuable and it has contributed to your research or projects, we kindly request that you cite our paper. Your recognition is a driving force for our continuous improvement and innovation🤗.

@misc{ma2024imdlbenco,
    title={IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization},
    author={Xiaochen Ma and Xuekang Zhu and Lei Su and Bo Du and Zhuohang Jiang and Bingkui Tong and Zeyu Lei and Xinyu Yang and Chi-Man Pun and Jiancheng Lv and Jizhe Zhou},
    year={2024},
    eprint={2406.10580},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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