Abstract. We introduce a novel deep neural architecture for image
copy-move forgery detection (CMFD), code-named BusterNet. Unlike
previous efforts, BusterNet is a pure, end-to-end trainable, deep neural network solution. It features a two-branch architecture followed by
a fusion module. The two branches localize potential manipulation regions via visual artifacts and copy-move regions via visual similarities,
respectively. To the best of our knowledge, this is the first CMFD algorithm with discernibility to localize source/target regions. We also propose simple schemes for synthesizing large-scale CMFD samples using
out-of-domain datasets, and stage-wise strategies for effective BusterNet
training. Our extensive studies demonstrate that BusterNet outperforms
state-of-the-art copy-move detection algorithms by a large margin on
the two publicly available datasets, CASIA and CoMoFoD, and that it
is robust against various known attacks