[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

• [2024/12/10] Mesorch, our new IML backbone model, which adopts a parallel CNN+Transformer structure to simultaneously deal with image semantics and non-semantics, is accepted by AAAI 25!!!🎉🎉🎉
• [2024/12/10] Sparse-ViT, the very first solution of constructing the non-semantic feature extractor through a self-supervised manner in IML is proposed by us and accepted by AAAI 25!!!🎉🎉🎉
• [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:

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:

• IMDL-BenCo's project leader/supervisor is Associate Professor 🏀Jizhe Zhou (周吉喆), Sichuan University🇨🇳.
• IMDL-BenCo's codebase designer and coding leader is the Research Assitant Xiaochen Ma (马晓晨), Sichuan University🇨🇳.
• IMDL-BenCo is jointly sponsored and advised by Prof. Jiancheng LV (吕建成), Sichuan University 🐼, and Prof. Chi-Man PUN (潘治文), University of Macau 🇲🇴, through the Research Center of Machine Learning and Industry Intelligence, China MOE platform.

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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