Exposing Digital Forgeries by Detecting a Contextual Violation Using Deep Neural Networks

  • Jong-Uk Hou
  • Han-Ul Jang
  • Jin-Seok Park
  • Heung-Kyu LeeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10763)


Previous digital image forensics focused on the low-level features that include traces of the image modifying history. In this paper, we present a framework to detect the manipulation of images through a contextual violation. First, we proposed a context learning convolutional neural networks (CL-CNN) that detects the contextual violation in the image. In combination with a well-known object detector such as R-CNN, the proposed method can evaluate the contextual scores according to the combination of objects in the image. Through experiments, we showed that our method effectively detects the contextual violation in the target image.



This work was supported by the Institute for Information & communications Technology Promotion (IITP) grant funded by the Korean government (MSIP) (2017-0-01671, Development of high reliability elementary image authentication technology for smart media environment).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jong-Uk Hou
    • 1
  • Han-Ul Jang
    • 1
  • Jin-Seok Park
    • 1
  • Heung-Kyu Lee
    • 1
    Email author
  1. 1.School of ComputingKAISTDaejeonRepublic of Korea

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