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BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization

  • Yue WuEmail author
  • Wael Abd-Almageed
  • Prem Natarajan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11210)

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.

Keywords

Copy-move Image forgery detection Deep learning 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.USC Information Sciences InstituteMarina del ReyUSA
  2. 2.Amazon AlexaCambridgeUSA

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