  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
