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.
CITATION STYLE
Wu, Y., Abd-Almageed, W., & Natarajan, P. (2018). BusterNet: Detecting copy-move image forgery with source/target localization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11210 LNCS, pp. 170–186). Springer Verlag. https://doi.org/10.1007/978-3-030-01231-1_11
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