Improving Robustness of Image Tampering Detection for Compression

EasyChair Preprint 620

Abstract

The task of verifying the originality and authenticity of images puts numerous constraints on what tampering detection algorithms should be able to achieve. Since most images are acquired on the Internet, there is a significant probability that they have undergone transformations such as compression, noising, re-sizing 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.

Keyphrases: Camera Model Identification, Convolutional Neural Networks, image forensics, lossy compression

@booklet{EasyChair:620,
  author    = {Boubacar Diallo and Thierry Urruty and Pascal Bourdon and Christine Fernandez-Maloigne},
  title     = {Improving Robustness of Image Tampering Detection for Compression},
  doi       = {10.29007/p71c},
  howpublished = {EasyChair Preprint 620},
  year      = {EasyChair, 2018}}