  Image forgery is a topic that has been studied for many years. Before the
breakthrough of deep learning, forged images were detected using handcrafted
features that did not require training. These traditional methods failed to
perform satisfactorily even on datasets much worse in quality than real-life
image manipulations. Advances in deep learning have impacted image forgery
detection as much as they have impacted other areas of computer vision and have
improved the state of the art. Deep learning models require large amounts of
labeled data for training. In the case of image forgery, labeled data at the
pixel level is a very important factor for the models to learn. None of the
existing datasets have sufficient size, realism and pixel-level labeling at the
same time. This is due to the high cost of producing and labeling quality
images. It can take hours for an image editing expert to manipulate just one
image. To bridge this gap, we automate data generation using image composition
techniques that are very related to image forgery. Unlike other automated data
generation frameworks, we use state of the art image composition deep learning
models to generate spliced images close to the quality of real-life
manipulations. Finally, we test the generated dataset on the SOTA image
manipulation detection model and show that its prediction performance is lower
compared to existing datasets, i.e. we produce realistic images that are more
difficult to detect. Dataset will be available at
https://github.com/99eren99/DIS25k .
