Copy-Move Forgery Detection Based on Local Gabor Wavelets Patterns

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 733)

Abstract

Nowadays digital images are more and more easily to be modified or tampered intentionally by most people due to the rapid development of powerful image processing software. Various methods of digital image forgery exist, such as image splicing, copy-move forgery, and image retouching. Copy-move is one of the typical image forgery methods, in which a part of an image is duplicated and used to replace another part of the same image at a different location. In this paper, we proposed a block-based passive detect copy-move forgery detection method based on local Gabor wavelets patterns (LGWP) with the advantages of high performance texture analysis of Gabor filter and rotation-invariant ability of uniform local binary pattern (LBP). Experiment results demonstrate the ability of the proposed method to detect copy-move forgery and precisely locate the duplicated regions, even when the forgery images are distorted by JPEG compression, blurring, brightness adjustment and rotation.

Keywords

Copy-move forgery Image forgery detection Local Gabor wavelets patterns (LGWP) 

References

  1. 1.
    Warif, N.B.A., Wahab, A.W.A., Idris, M.Y.I., Ramli, R., Salleh, R., Shamshirband, S., Choo, K.-K.R.: Copy-move forgery detection: survey, challenges and future directions. J. Netw. Comput. Appl. 75, 259–278 (2016)CrossRefGoogle Scholar
  2. 2.
    Fridrich, J., Soukal, D., Lukas, J.: Detection of copy–move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop, pp. 19–23 (2003)Google Scholar
  3. 3.
    Popescu A., Farid, H.: Exposing digital forgeries by detecting duplicated image regions. Technical report TR2004-515, Department of Computer Science, Dartmouth College (2004)Google Scholar
  4. 4.
    Hsu, H.C., Wang, M.S.: Detection of copy-move forgery image using Gabor descriptor. In: Proceedings of International Conference on Anti-Counterfeiting, Security and Identification (ASID), pp. 1–4 (2012)Google Scholar
  5. 5.
    Lee, J.-C.: Copy-move image forgery detection based on Gabor magnitude. J. Vis. Commun. Image Represent. 31, 320–334 (2015)CrossRefGoogle Scholar
  6. 6.
    Davarzani, R., Yaghmaie, K., Mozaffari, S., Tapak, M.: Copy-move forgery detection using multiresolution local binary patterns. Forensic Sci. Int. 231, 61–72 (2013)CrossRefGoogle Scholar
  7. 7.
    Amerini, I., Ballan, L., Caldelli, R., Bimbo, A.D., Serra, G.: A SIFT based forensic method for copy-move attack detection and transformation recovery. IEEE Trans. Inf. Forensics Secur. 6(3), 1099–1110 (2011)CrossRefGoogle Scholar
  8. 8.
    Christlein, V., Riess, C., Jordan, J., Riess, C., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012)CrossRefGoogle Scholar
  9. 9.
    Bo, X., Junwen, W., Guangjie, L., Yuewei, D.: Image copy-move forgery detection based on SURF. In: Proceedings of International Conference on Multimedia Information Networking and Security, pp. 889–892 (2010)Google Scholar
  10. 10.
    Daugman, J.: Two-dimensional analysis of cortical receptive field profiles. Vision. Res. 20, 846–856 (1980)CrossRefGoogle Scholar
  11. 11.
    Ojala, T., Pietikainen, M., Maèenpaèa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  12. 12.
    CoMoFoD database Homepage. http://www.vcl.fer.hr/comofod. Accessed 23 Oct 2017

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and Information Engineering, Chung Cheng Institute of TechnologyNational Defense UniversityTaoyuanTaiwan
  2. 2.Department of Electrical EngineeringChinese Naval AcademyKaohsiungTaiwan

Personalised recommendations