Abstract

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

Date

2018

Authors

Yue Wu, Wael Abd-Almageed, Prem Natarajan

Conference

Proceedings of the European conference on computer vision (ECCV)

Pages

168-184