Comparative Study of Image Forgery and Copy-Move Techniques

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)

Abstract

Image forgery means manipulation of the digital image to conceal some meaningful or useful information of the image. There are cases when it is difficult to identify the edited region from the original image. The detection of a forged image is driven by the need of authenticity and to maintain integrity of the image. This paper surveys different types of image forgeries. The survey has been done on existing techniques for forged image and it also highlights various copy – move detection methods based on their robustness and computational complexity.

Keywords

Image forgery Copy-move detection Active approach passive approach Robust Geometric transformation 

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References

  1. 1.
    Huang, H., Guo, W., Zhang, Y.: Detection of Copy-move forgery in digital image using SIFT algorithm. In: IEEE Pacific–Asia Workshop on Computational Intelligence and Industrial Application, pp. 272–276. IEEE computer society (2008)Google Scholar
  2. 2.
    Kang, L., Cheng, X.-P.: Copy – move forgery detection in digital image. In: 3rd International Congress on Image and Signal Processing (CISP), pp. 2419–2421 (2010)Google Scholar
  3. 3.
    Edupuanti, V.G., Shih, F.Y.: Authentication of JPEG Images based on Genetic Algorithms. The Open Artifical Intelligence Journal 4, 30–36 (2010)CrossRefGoogle Scholar
  4. 4.
    Ardizzone, E., Bruno, A., Mazzola, G.: Copy – move forgery detection via texture description, pp. 59–64. ACM (2010)Google Scholar
  5. 5.
    Yang, Q.-C., Huang, C.-L.: Copy – Move Forgery detection in digital image. Springer, Heidelberg (2009)Google Scholar
  6. 6.
    Fridrich, J., Soukal, D., Lukas, J.: Detection of copy- move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop, Cleveland (August 2003)Google Scholar
  7. 7.
    Bayram, S., Sencar, H.T., Memon, N.: An efficient and robust method for detecting copy-Move forgery. In: International Conference on Acoustics, Speech and Signal Processing, pp. 1053–1056 (2009)Google Scholar
  8. 8.
    Math, S., Tripathi, R.C.: Image quality feature based detection algorithm for forgery in images. International Journal of Computer graphics and animation (IJCGA) 1(1), 13–21 (2011)Google Scholar
  9. 9.
    Zimba, M., Sun, X.: DWT- PCA (EVD) based copy – move image forgery detection. International Journal of Digital Content Technology and Its Applications 5(1), 251–257 (2011)CrossRefGoogle Scholar
  10. 10.
    Gopi, E.S., Lakshmanan, N., Gokul, T., Kumaraganesh, S., Shah, P.R.: Digital image forgery detection using artificial neural network and auto regressive coefficients. In: IEEE CCECE/CCGEI, pp. 194–197 (2006)Google Scholar
  11. 11.
    Khan, S., Kulkarni, A.: Robust Method for detection of copy- move forgery in digital images, pp. 69–73. IEEE (2010)Google Scholar
  12. 12.
    Kumar, S., Da, P.K., Shally: Copy – Move Forgery detection in Digital Images: Progress and challenges. In: International Conference on Computer Science and Engineering (IJCSE), vol. 3(2) (February 2011)Google Scholar
  13. 13.
    Christlein, V., Riess, C., Angelopoulou, E.: A study on features for the detection of copy – move forgeriesGoogle Scholar
  14. 14.
    Farid, H.: Image Forgery Detection – A Survey. IEEE Signal Processing Magazine, 16–25 (2009)Google Scholar
  15. 15.
    Bayram, S., Sencar, H.T., Memon, N.: A survey of copy-Move forgery detection techniques. In: IEEE Western New York Image Processing Workshop, New York (September 2008)Google Scholar
  16. 16.
    Shih, F.Y., Yuan, Y.: A comparison study on copy – cover image forgery detection. The Open Artificial Intelligence Journal 4, 49–54 (2010)CrossRefGoogle Scholar
  17. 17.
    Shivakumar, B.L., Santhose Baboo, S.: Detecting copy – move forgery in digital images: A survey and analysis of current methods. Global Journal of Computer Science and Technology 10(7), 61–65 (2010)Google Scholar
  18. 18.
    Wiedermann, J.: The complexity of Lexicographic sorting and search. Computing Research CenterGoogle Scholar
  19. 19.
    Talbert, D.A., Fisher, D.: An empirical analysis of techniques for constructing and searching k-dimensional trees. In: International Conference on Knowledge Discovery and Data Mining, pp. 26–33 (2000)Google Scholar
  20. 20.
    Jung, I.K., Lacroix, S.: A robust interest point matching algorithm. In: International Conference on Computer Vision (2001)Google Scholar
  21. 21.
    Mahdian, B., Saic, S.: Detection of copy – move forgery using a method based on blur moment invariants. In: Forensic Science International Conference, pp. 180–189 (2007)Google Scholar
  22. 22.
    Flusser, J., Suk, T., Saic, S.: Image features invariant with respect to blur. Patten Recognisation, 1723–1732 (1995)Google Scholar
  23. 23.
    Cox, I.J., Miller, M.L., Bloom, J.A.: Digital watermarking principles and practices (2002)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringNational Institute of TechnologyTiruchirappallaiIndia

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