The evaluation datasets and metrics for image manipulation detection and
localization (IMDL) research have been standardized. But the training dataset
for such a task is still nonstandard. Previous researchers have used
unconventional and deviating datasets to train neural networks for detecting
image forgeries and localizing pixel maps of manipulated regions. For a fair
comparison, the training set, test set, and evaluation metrics should be
persistent. Hence, comparing the existing methods may not seem fair as the
results depend heavily on the training datasets as well as the model
architecture. Moreover, none of the previous works release the synthetic
training dataset used for the IMDL task. We propose a standardized benchmark
training dataset for image splicing, copy-move forgery, removal forgery, and
image enhancement forgery. Furthermore, we identify the problems with the
existing IMDL datasets and propose the required modifications. We also train
the state-of-the-art IMDL methods on our proposed TrainFors1 dataset for a fair
evaluation and report the actual performance of these methods under similar
conditions.

我们提出了一个标准化的基准训练数据集，用于图像拼接、复制移动伪造、去除伪造和图像增强伪造的 IMDL 任务，并对现有 IMDL 数据集的问题进行了修改。我们还在我们提出的 TrainFors1 数据集上对最先进的 IMDL 方法进行了训练，以公平评估并报告了这些方法在类似条件下的实际性能。