Improved DSIFT Descriptor Based Copy-Rotate-Move Forgery Detection

  • Ali Retha Hasoon Khayeat
  • Xianfang Sun
  • Paul L. Rosin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9431)


In recent years, there has been a dramatic increase in the number of images captured by users. This is due to the wide availability of digital cameras and mobile phones which are able to capture and transmit images. Simultaneously, image-editing applications have become more usable, and a casual user can easily improve the quality of an image or change its content. The most common type of image modification is cloning, or copy-move forgery (CMF), which is easy to implement and difficult to detect. In most cases, it is hard to detect CMF with the naked eye and many possible manipulations (attacks) can be used to make the doctored image more realistic. In CMF, the forger copies part(s) of the image and pastes them back into the same image. One possible transformation is rotation, where an object is copied, rotated and pasted. Rotation-invariant features need to be used to detect Copy-Rotate-Move (CRM) forgery. In this paper we present three contributions. First, a new technique to detect CMF is developed, using Dense Scale-Invariant Feature Transform (DSIFT). Second, a new improved DSIFT descriptor is implemented which is more robust to rotation than Zernike moments. Third, a new method to remove false matching is proposed. Extensive experiments have been conducted to train, evaluate and test the algorithms, the new feature vector and the suggested method to remove false matching. We show that the proposed method can detect forgery in images with blurring, brightness change, colour reduction, JPEG compression, variations in contrast and added noise.


Copy-move forgery Copy-rotate-move DSIFT descriptor Zernike moments 



This research was supported by the Higher Committee for Education Development (HCED) in Iraq and the School of Computer Science and Informatics, Cardiff University.


  1. 1.
    Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Serra, G.: A SIFT-based forensic method for copy-move attack detection, transformation recovery. IEEE Trans. Inf. Forensics Secur. 6((3 PART 2)), 1099–1110 (2011)CrossRefGoogle Scholar
  2. 2.
    Baboo, S.: Automated forensic method for copy-move forgery detection based on Harris interest points and SIFT descriptors. Int. J. Comput. Appl. 27(3), 9–17 (2011)Google Scholar
  3. 3.
    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
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  5. 5.
    Dong, J., Wang, W., Tan, T.: Casia image tampering detection evaluation database. In: 2013 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP), pp. 422–426. IEEE (2013)Google Scholar
  6. 6.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Fridrich, A.J., Soukal, B.D., Lukáš, A.J.: Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop. Citeseer (2003)Google Scholar
  8. 8.
    Huang, H., Guo, W., Zhang, Y.: Detection of copy-move forgery in digital images using SIFT algorithm. In: 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, vol. 2, pp. 272–276. IEEE, December 2008Google Scholar
  9. 9.
    Li, J., Li, X., Yang, B., Sun, X.: Segmentation-based image copy-move forgery detection scheme. IEEE Trans. Inf. Forensics Secur. 10(3), 507–518 (2015)CrossRefGoogle Scholar
  10. 10.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  11. 11.
    Ng, T.-T., Chang, S.-F., Hsu, J., Pepeljugoski, M.: Columbia photographic images and photorealistic computer graphics dataset. ADVENT Tecnical report # 205–2004-5, Columbia University, pp. 1–23 (2005)Google Scholar
  12. 12.
    Pan, X., Lyu, S.: Region duplication detection using image feature matching. IEEE Trans. Inf. Forensics Secur. 5, 857–867 (2010)CrossRefGoogle Scholar
  13. 13.
    Prokop, R.J., Reeves, A.P.: A survey of moment-based techniques for unoccluded object representation and recognition. CVGIP Graph. Models Image Process. 54, 438–460 (1992)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    Lee, H.-K., Lee, M.-J., Ryu, S.-J.: Detection of copy-rotate-move forgery using Zernike moments. In: Böhme, R., Fong, P.W.L., Safavi-Naini, R. (eds.) IH 2010. LNCS, vol. 6387, pp. 51–65. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Tralic, D., Zupancic, I., Grgic, S., Grgic, M.: CoMoFoD - new database for copy-move forgery detection. In: Proceedings of 55th International Symposium ELMAR-2013, Number September, pp. 25–27. IEEE (2013)Google Scholar
  17. 17.
    Zuliani, M.: RANSAC for Dummies With examples using the RANSAC toolbox for Matlab\(^{\rm TM}\) & Octave and more. I Edizione (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ali Retha Hasoon Khayeat
    • 1
    • 2
  • Xianfang Sun
    • 1
  • Paul L. Rosin
    • 1
  1. 1.School of Computer Science and InformaticesCardiff UniversityCardiffUK
  2. 2.Computer Science Department, College of ScienceKerbala UniversityKerbalaIraq

Personalised recommendations