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Multimedia Tools and Applications

, Volume 77, Issue 10, pp 11823–11842 | Cite as

A robust forgery detection algorithm for object removal by exemplar-based image inpainting

  • Dengyong Zhang
  • Zaoshan Liang
  • Gaobo Yang
  • Qingguo Li
  • Leida Li
  • Xingming Sun
Article
  • 448 Downloads

Abstract

Object removal is a malicious image forgery technique, which is usually achieved by exemplar-based image inpainting in a visually plausible way. Most existing forgery detection approaches utilize similar block pairs between inpainted area and the rest areas, but they invalidate when those inpainted images are further subjected to some post-processing operations such as JPEG compression, Gaussian noise addition and blurring. It is desirable to develop a forensic method which is robust to object removal with post-processing. From some preliminary experiments, we observe that post-processing destroys the similarity of block pairs and simultaneously disturbs the correlations among adjacent pixels to some extent. Inspired by the strong ability of joint probability density matrix (JPDM) in characterizing such correlation, we propose a hybrid forensics strategy. Firstly, our earlier method is employed to detect whether a candidate image is forged or not. Secondly, for those undetected images after the first step, JPDM is computed for each difference array to model the correlations among adjacent DCT coefficients, and the average of these matrixes are computed as feature vectors to further expose tampering traces. Experimental results show that the proposed approach can effectively detect object removal by exemplar-based inpainting either with or without post-processing.

Keywords

Passive image forensics Exemplar-based image inpainting Post-processing Joint probability density matrix 

Notes

Acknowledgements

We would like to thank the anonymous reviewers for their professional comments and valuable suggestions. This work is partially or fully sponsored by National Natural Science Foundation of China (61572183, 61379143), the Specialized Research Fund for the Doctoral Program of Higher Education (20120161110014), the Scientific Research Fund of Hunan Provincial Education Department of China (14C0029), Natural Science Foundation of Hunan Province (2016JJ2005). The authors appreciate the nice help from Mr Moses Odero for his improving the English usages.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Dengyong Zhang
    • 1
    • 2
  • Zaoshan Liang
    • 1
  • Gaobo Yang
    • 1
  • Qingguo Li
    • 3
  • Leida Li
    • 4
  • Xingming Sun
    • 5
  1. 1.School of Information Science and EngineeringHunan UniversityChangshaChina
  2. 2.School of Computer & Communication EngineeringChangsha University of Science & TechnologyChangshaChina
  3. 3.College of Mathematics and EconomicsHunan UniversityChangshaChina
  4. 4.School of Information and Electrical EngineeringChina University of Mining and TechnologyXuzhouChina
  5. 5.School of Computer and SoftwareNanjing University of Information Science & TechnologyNanjingChina

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