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IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization by Ma, Xiaochen; Zhu, Xuekang; Su, Lei; Du, Bo; Jiang, Zhuohang; Tong, Bingkui; Lei, Zeyu; Yang, Xinyu; Pun, Chi-Man; Lv, Jiancheng; Zhou, Jizhe is a scholarly article available to read on EtoBox.

What is IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization about?

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: i) decomposes the IMDL framework into standardized, reusable components and revises the model construction pipeline, improving coding efficiency and customization flexibility; ii) fully implements or incorporates training code for state-of-the-art models to establish a comprehensive IMDL benchmark; and 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)

Author

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

Published

2024

Language

EN