  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.
