A natural image model approach to splicing detection

  • Authors:

  • Yun Q. Shi;Chunhua Chen;Wen Chen

  • Affiliations:

  • New Jersey Institute of Technology, Newark, NJ;New Jersey Institute of Technology, Newark, NJ;New Jersey Institute of Technology, Newark, NJ

  • Venue:
  • Proceedings of the 9th workshop on Multimedia & security
  • Year:
  • 2007

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Abstract

Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this paper, we propose a blind, passive, yet effective splicing detection approach based on a natural image model. This natural image model consists of statistical features extracted from the given test image as well as 2-D arrays generated by applying to the test images multi-size block discrete cosine transform (MBDCT). The statistical features include moments of characteristic functions of wavelet subbands and Markov transition probabilities of difference 2-D arrays. To evaluate the performance of our proposed model, we further present a concrete implementation of this model that has been designed for and applied to the Columbia Image Splicing Detection Evaluation Dataset. Our experimental works have demonstrated that this new splicing detection scheme outperforms the state of the art by a significant margin when applied to the above-mentioned dataset, indicating that the proposed approach possesses promising capability in splicing detection.