Nonlinear Dynamics

, Volume 84, Issue 1, pp 341–353 | Cite as

Application of AFMT method for composite forgery detection

Original Paper

Abstract

In modern society, as the important medium of information transfer, digital image plays a more and more important role in our daily life. With the modern science and technology revolution, a social phenomenon that people without any professional technique can easily forge and process digital images become commonplace. The image composite forgery, also called copy–move forgery, is the most popular image forged operation. Mostly existing methods are inept for the detection of the composite forgery image underwent geometric distortions. This paper presents a robust and efficient analytical Fourier–Mellin transform (AFMT)-based method. The focus of AFMT method is to construct the scaling and rotation invariant and extract its invariances for the detection of composite forgery. First, the general AFMT expression is given. The radial complex exponential kernel of AFMT is discussed to get the orthogonal feature. Then, the invariant to scaling and rotation is presented to construct the image geometric moment invariants. To extract the scaling and rotation invariance of each pixel of detecting image, a disk template is applied for sliding on the detected image and calculating geometric invariant features. After extracting geometric features, useful geometric features are further filtered from image background information. Then, correlational features of pixels are sorted by lexicographic sorting. Pearson correlation coefficient is applied for identifying the similar continuous regions and locating their positions. Finally, the detected suspicious composite regions are marked. Extensive experiments have been performed to show that the presented AFMT method can detect the composite region in the forgery image precisely. It is also proven that it is more robust and efficient than other existing relevant methods.

Keywords

Geometric distortions Analytical Fourier–Mellin Transform The detection of composite forgery Disk template Geometric invariant 

References

  1. 1.
    Zhang, Z., Ren, Y., Ping, X.J., He, Z.Y., Zhang, S.Z.: A survey on passive-blind image forgery by doctor method detection. In: Proceedings 2008 International Conference on Machine Learning and Cybernetics, vol. 6, pp. 3463–3467 (2008)Google Scholar
  2. 2.
    Farid, H.: A survey of image forgery detection. IEEE Signal Process. Mag. 26(2), 16–25 (2009)CrossRefGoogle Scholar
  3. 3.
    Kang, L., Cheng, X.P.: Copy–move forgery detection in digital image. In: proceedings 2010 3rd International Congress on Image and Signal Processing (CISP), vol. 5, pp. 2419–2421 (2010)Google Scholar
  4. 4.
    Fridrich, J., Soukal, D., Lukás, J.: Detection of copy–move forgery in digital images. In: Proceedings of the Digital Forensic Research Workshop, pp. 55–61 (2003)Google Scholar
  5. 5.
    Popescu, A.C.: Exposing digital forgeries by detecting traces of resampling. IEEE Trans. Signal Process. 53(2), 758–767 (2005)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Sutcu, Y., Coskun, B., Sencar, H.T., Memon, N.: Tamper detection based on regularity of wavelet transform coefficients. In: Proceedings of IEEE International Conference on Image Processing (ICIP), pp. 397–400 (2007)Google Scholar
  7. 7.
    Kashyap, A., Joshi, S.D.: Detection of copy–move forgery using wavelet decomposition. In: Proceedings of International Conference on Signal Processing and Communication (ICSC), pp. 396–400 (2013)Google Scholar
  8. 8.
    Hu, M.K.: Visual pattern recognition by moment invariants. IEEE Trans. Inf. Theory 8(2), 179–187 (1962)CrossRefMATHGoogle Scholar
  9. 9.
    Liao, S.X., Pawlak, M.: On the accuracy of Zernike moments for image analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1358–1364 (1998)CrossRefGoogle Scholar
  10. 10.
    Amerini, I., Ballan, L., Caldelli, R., Bimbo, A.D.: A sift-based forensic method for copy–move attack detection and transformation recovery. IEEE Trans. Inf. Forensics Secur. 6(9), 1099–1110 (2011)CrossRefGoogle Scholar
  11. 11.
    Yap, P.T., Jiang, X.D., Kot, A.C.: Two-dimensional polar harmonic transforms for invariant image representation. IEEE Trans. Pattern Anal. Mach. Intell. 32(7), 1260–1270 (2010)Google Scholar
  12. 12.
    Huang, Z.H., Leng, J.S.: Analysis of Hu’s moment invariants on image scaling and rotation. In: proceedings of the 2nd International Conference on Computer Engineering and Technology (ICCET), vol. 7, pp.476–480 (2010)Google Scholar
  13. 13.
    Ryu, S.J., Kirchner, M., Lee, M.J., Lee, H.K.: Rotation invariant localization of duplicated image regions based on zernike moments. IEEE Trans. Inf. Forensics Secur. 8(8), 1355–1370 (2013)CrossRefGoogle Scholar
  14. 14.
    Li, L.D., Li, S.S., Wang, J.: Copy–move forgery detection based on PHT. In: Proceedings World Congress on Information and Communication Technologies, pp. 1061–1065 (2012)Google Scholar
  15. 15.
    Zhong, L., Xu, W.H.: A robust image copy–move forgery detection based on mixed moments. In: Proceedings of the 4th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 381–384 (2013)Google Scholar
  16. 16.
    Bayram, S., Sencar, H.T., Memon, N.: An efficient and robust method for detecting copy–move forgery. In: Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.1053–1056 (2009)Google Scholar
  17. 17.
    Sellami, M., Ghorbe, F.: An invariant similarity registration algorithm based on the analytical Fourier–Mellin Transform. In: Proceedings of the 20th European Signal Processing Conference (EUSIPCO), pp. 390–394 (2012)Google Scholar
  18. 18.
    Wang, J.W., Liu, G.J., Li, H.Y., D Y.W., Wang, Z.Q.: Detection of image region duplication forgery using model with circle block. In: Proceedings International Conference on Multimedia Information Networking and Security (MINES2009), pp. 25–29 (2009)Google Scholar
  19. 19.
    Luo, W.Q., Huang, J.W., Qiu, G.P.: Robust detection of region duplication forgery in digital image. Chin. J. Comput. 30(11), 1998–2007 (2007)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Department of Information Science and TechnologyGuangdong University of Foreign Studies South China Business CollegeGuangzhouPeople’s Republic of China
  2. 2.School of Information EngineeringGuangdong Mechanical and Electrical CollegeGuangzhouPeople’s Republic of China

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