A natural image model approach to splicing detection

Yun-Qing Shi, Chunhua Chen, Wen Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

163 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMM and Sec'07 - Proceedings of the Multimedia and Security Workshop 2007
Pages51-62
Number of pages12
DOIs
StatePublished - Dec 1 2007
EventMM and Sec'07 - 9th Multimedia and Security Workshop 2007 - Dallas, TX, United States
Duration: Sep 20 2007Sep 21 2007

Publication series

NameMM and Sec'07 - Proceedings of the Multimedia and Security Workshop 2007

Other

OtherMM and Sec'07 - 9th Multimedia and Security Workshop 2007
Country/TerritoryUnited States
CityDallas, TX
Period9/20/079/21/07

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

Keywords

  • Block discrete cosine transform
  • Digital forensics
  • Markov process
  • Splicing detection
  • Statistical moment

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