  In existing splicing forgery datasets, the insufficient semantic varieties of
spliced regions cause a problem that trained detection models overfit semantic
features rather than splicing traces. Meanwhile, because of the absence of a
reasonable dataset, different detection methods proposed cannot reach a
consensus on experimental settings. To address these urgent issues,
GreatSplicing, a manually created splicing dataset with a considerable amount
and high quality, is proposed in this paper. GreatSplicing comprises 5,000
spliced images and covers spliced regions with 335 distinct semantic
categories, allowing neural networks to grasp splicing traces better. Extensive
experiments demonstrate that models trained on GreatSplicing exhibit minimal
misidentification rates and superior cross-dataset detection capabilities
compared to existing datasets. Furthermore, GreatSplicing is available for all
research purposes and can be downloaded from www.greatsplicing.net.
