New developments in image tampering detection

Guanshuo Xu, Jingyu Ye, Yun Qing Shi

Research output: Contribution to journalConference articlepeer-review


Statistical feature-based pattern recognition approach has been proved to be useful in digital image forgery detection. In this paper, we first present this approach adopted in Phase 1 of the first IEEE IFS-TC Image Forensics Challenge, in which the task is to classify the tampered images from the original ones, together with the experimental results. Several different kinds of statistical features and their combinations have been tested. Finally, we have chosen to use co-occurrence matrices calculated from the rich model of noise residual images. Furthermore we have selected a subset of the rich model to further enhance the testing accuracy by about 2% so as to reach a high detection accuracy of 93.7%. In Phase 2 of the competition, the task is to localize the tampered regions by identifying the tampered pixels. For this purpose, we have introduced the Hamming distance of Local Binary Patterns as similarity measure to tackle the tampering without post-processing on copy-moved regions. The PatchMatch algorithm has been adopted as an efficient search algorithm for block-matching. We have also applied a simple usage of the scale-invariant feature transform (SIFT) when other kinds of processing such as rotation and scaling have been performed on the copy-moved region. The achieved f-score in identifying tampered pixels is 0.267. In summary, some success has been achieved apparently. However, much more efforts are called for to move image tampering detection ahead.

Original languageEnglish (US)
Pages (from-to)3-17
Number of pages15
JournalConference Proceedings of the Society for Experimental Mechanics Series
StatePublished - 2015
Event13th International Workshop on Digital-Forensics and Watermarking , IWDW 2014 - Taipei, Taiwan, Province of China
Duration: Oct 1 2014Oct 4 2014

All Science Journal Classification (ASJC) codes

  • General Engineering
  • Computational Mechanics
  • Mechanical Engineering


  • Digital forensics
  • Forgery detection
  • Image tampering detection
  • Natural image modeling


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