IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization
<|reference_start|>IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization: A comprehensive benchmark is yet to be established in the Image Manipulation Detection \& Localization (IMDL) field. The absence of such a benchmark leads to insufficient and misleading model evaluations, severely undermining the development of this field. However, the scarcity of open-sourced baseline models and inconsistent training and evaluation protocols make conducting rigorous experiments and faithful comparisons among IMDL models challenging. To address these challenges, we introduce IMDL-BenCo, the first comprehensive IMDL benchmark and modular codebase. IMDL-BenCo:~\textbf{i)} decomposes the IMDL framework into standardized, reusable components and revises the model construction pipeline, improving coding efficiency and customization flexibility;~\textbf{ii)} fully implements or incorporates training code for state-of-the-art models to establish a comprehensive IMDL benchmark; and~\textbf{iii)} conducts deep analysis based on the established benchmark and codebase, offering new insights into IMDL model architecture, dataset characteristics, and evaluation standards. Specifically, IMDL-BenCo includes common processing algorithms, 8 state-of-the-art IMDL models (1 of which are reproduced from scratch), 2 sets of standard training and evaluation protocols, 15 GPU-accelerated evaluation metrics, and 3 kinds of robustness evaluation. This benchmark and codebase represent a significant leap forward in calibrating the current progress in the IMDL field and inspiring future breakthroughs. Code is available at: https://github.com/scu-zjz/IMDLBenCo<|reference_end|>
@article{ma2024imdl-benco:,
    title={IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image
  Manipulation Detection & Localization},
    author={Xiaochen Ma, Xuekang Zhu, Lei Su, Bo Du, Zhuohang Jiang, Bingkui Tong,
  Zeyu Lei, Xinyu Yang, Chi-Man Pun, Jiancheng Lv, Jizhe Zhou},
    journal={arXiv preprint arXiv:2406.10580},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10580},
    primaryClass={cs.CV}
}