Detection Algorithm for Copy-Move Forgery Based on Circle Block

  • Choudhary Shyam PrakashEmail author
  • Sushila Maheshkar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 460)


Today lots of software tools are available which are used to manipulate the images easily to change their originality. The technique which is usually used these days for tampering an image without leaving any microscopic evidence is copy-move forgery. There are many existing techniques to detect image tampering but their computational complexity is high. Here we present a robust and effective technique to find the tampered region. Initially the given image is divided into fixed size blocks and DCT is applied on each block for feature extraction. Circle is used to represent each transformed block with two feature vectors. In this way we reduce the dimension of the blocks to extract the feature vectors. Then lexicographical sort is applied to sort the extracted feature vectors. Matching algorithm is applied to detect the tampered regions. Results show that our algorithm is robust and has less computational complexity than the existing one.


Image forensics Copy-Move forgery Dimension reduction Circle block Region duplication detection 


  1. 1.
  2. 2.
    Bayram, S., Sencar, H.T., Memon, N.: An efficient and robust method for detecting copy-move forgery. In: Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on. pp. 1053–1056. IEEE (2009)Google Scholar
  3. 3.
    Cao, Y., Gao, T., Fan, L., Yang, Q.: A robust detection algorithm for copy-move forgery in digital images. Forensic science international 214(1), 33–43 (2012)CrossRefGoogle Scholar
  4. 4.
    Christlein, V., Riess, C., Jordan, J., Riess, C., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. Information Forensics and Security, IEEE Transactions on 7(6), 1841–1854 (2012)CrossRefGoogle Scholar
  5. 5.
    Fridrich, A.J., Soukal, B.D., Lukáš, A.J.: Detection of copy-move forgery in digital images. In: in Proceedings of Digital Forensic Research Workshop. Citeseer (2003)Google Scholar
  6. 6.
    Huang, Y., Lu, W., Sun, W., Long, D.: Improved dct-based detection of copy-move forgery in images. Forensic science international 206(1), 178–184 (2011)CrossRefGoogle Scholar
  7. 7.
    Luo, W., Huang, J., Qiu, G.: Robust detection of region-duplication forgery in digital image. In: Pattern Recognition, 2006. ICPR 2006. 18th International Conference on. vol. 4, pp. 746–749. IEEE (2006)Google Scholar
  8. 8.
    Mahdian, B., Saic, S.: Detection of copy–move forgery using a method based on blur moment invariants. Forensic science international 171(2), 180–189 (2007)CrossRefGoogle Scholar
  9. 9.
    Pan, X., Lyu, S.: Detecting image region duplication using sift features. In: Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on. pp. 1706–1709. IEEE (2010)Google Scholar
  10. 10.
    Popescu, A., Farid, H.: Exposing digital forgeries by detecting duplicated image region [technical report]. 2004-515. Hanover, Department of Computer Science, Dartmouth College. USA (2004)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian School of MinesDhanbadIndia

Personalised recommendations