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Improving Robustness of Image Tampering Detection for Compression

  • Boubacar DialloEmail author
  • Thierry Urruty
  • Pascal Bourdon
  • Christine Fernandez-Maloigne
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)

Abstract

The task of verifying the originality and authenticity of images puts numerous constraints on tampering detection algorithms. Since most images are acquired on the internet, there is a significant probability that they have undergone transformations such as compression, noising, resizing and/or filtering, both before and after the possible alteration. Therefore, it is essential to improve the robustness of tampered image detection algorithms for such manipulations. As compression is the most common type of post-processing, we propose in our work a robust framework against this particular transformation. Our experiments on benchmark datasets show the contribution of our proposal for camera model identification and image tampering detection compared to recent literature approaches.

Keywords

Image forensics Lossy compression Camera model identification Convolutional neural networks 
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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Boubacar Diallo
    • 1
    Email author
  • Thierry Urruty
    • 1
  • Pascal Bourdon
    • 1
  • Christine Fernandez-Maloigne
    • 1
  1. 1.XLIM Research Institute (UMR CNRS 7252), University of PoitiersPoitiersFrance

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