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Copy-Move Detection Based on Different Forms of Local Binary Patterns

  • Andrey KuznetsovEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11179)

Abstract

An obvious way of digital image forgery is a copy-move attack. It is quite simple to carry out to hide important information in an image. Copy-move process contains three main steps: copy the fragment from one place of an image, transform it by some means and paste to another place of the same image. Nowadays researchers develop a lot of copy-move detection solutions though the achieved results are far from perfect. In this paper, it is proposed a comparison of different local binary patterns (LBP) forms in the task of copy-move detection: geometric local binary patterns (GLBP), binary gradient contours (BGC), local derivative patterns (LDP) and simple LBP forms. All these LBP-based solutions are used to create local features that are robust to contrast enhancement, additive Gaussian noise, JPEG compression, affine transform. All these solutions are different in the number of transforms and transform parameters range that can be detected by the algorithm. Another advantage of these features is low computational complexity. Conducted experiments show that GLBP-based features can be used to detect all 4 transforms with a wide range of transforms parameters. The proposed solution showed high precision and recall values during experimental research for wide ranges of transform parameters. Thus, it showed a meaningful improvement in detection accuracy.

Keywords

Forgery Copy-move Local binary pattern Binary gradient contour Geometric local binary pattern Local derivative pattern 

Notes

Acknowledgements

This work was supported by the Federal Agency of Scientific Organization (Agreement 007-3/43363/26) in parts “Copy-move detection scheme” and “Binary Gradient Contours” and by the Russian Foundation for Basic Research (no. 19-07-00138) in parts “Geometric Local Binary Pattern”, “Local Derivative Pattern” and “Experiments”.

References

  1. 1.
  2. 2.
  3. 3.
    Bayram, S., Sencar, H.T., Memon, N.: An efficient and robust method for detecting copy-move forgery. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1053–1056 (2009)Google Scholar
  4. 4.
    Bravo-Solorio, S., Nandi, A.K.: Exposing postprocessed copy–paste forgeries through transform-invariant features. IEEE Trans. Inf. Forensics Secur. 7, 1018–1028 (2012)CrossRefGoogle Scholar
  5. 5.
    Cao, Y., Gao, T., Fan, L., Yang, Q.: A robust detection algorithm for copy-move forgery in digital images. Forensic Sci. Int. 214, 33–43 (2012)CrossRefGoogle Scholar
  6. 6.
    Christlein, V., Riess, C., Jordan, J., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7, 1841–1854 (2012)CrossRefGoogle Scholar
  7. 7.
    Huang, Y., Lu, W., Sun, W., Long, D.: Improved DCT-based detection of copy-move forgery in images. Forensic Sci. Int. 206, 178–184 (2011)CrossRefGoogle Scholar
  8. 8.
    Mahdian, B., Saic, S.: Detection of copy–move forgery using a method based on blur moment invariants. Forensic Sci. Int. 171, 180–189 (2007)CrossRefGoogle Scholar
  9. 9.
    Muhammad, G., Hussain, M., Bebis, G.: Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digit. Invest. 9, 49–57 (2012)CrossRefGoogle Scholar
  10. 10.
    Kang, X., Wei, S.: Identifying tampered regions using singular value decomposition in digital image forensics. In: Proceedings of the 2008 IEEE International Conference on Computer Science and Software Engineering, pp. 926–930 (2008)Google Scholar
  11. 11.
    Fernández, A., Álvarez, M.X., Bianconi, F.: Image classification with binary gradient contours. Opt. Lasers Eng. 49(9–10), 1177–1184 (2011)CrossRefGoogle Scholar
  12. 12.
    Orjuela Vargas, S.A., Yañez Puentes, J.P., Philips, W.: The geometric local textural patterns (GLTP). In: Brahnam, S., Jain, L.C., Nanni, L., Lumini, A. (eds.) Local Binary Patterns: New Variants and Applications. Studies in Computational Intelligence, vol. 506, pp. 85–112. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-642-39289-4_4CrossRefGoogle Scholar
  13. 13.
    Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans. Image Process. 19(2), 533–544 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Samara National Research UniversitySamaraRussia

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