KAZE Feature Based Passive Image Forgery Detection

  • D. VaishnaviEmail author
  • G. N. Balaji
  • D. Mahalakshmi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 815)


Copy-move image forgery is a most common tampering artifact. It can be carried out by copy-pasting a region of the same image; thus, it has become a challenging one to find. So, this paper put forwarding a method to detect such forgery by extracting the KAZE features. RANSAC algorithm is functioned to get rid off the false matches such as outliers, and then the forged image is disclosed. The experiment is carried out using the publically available datasets, and their performances are quantitatively assessed using the true positive rate and false positive rate. A comparative analysis is also done with state-of-the-art methods, and it is certified that the proposed method produced good results than the other methods.


Copy-move forgery KAZE RANSAC Clustering TPR FPR 



The authors express their gratitude and credits for the use of the MICC-F220 and MICC-F2000 databases.


  1. 1.
    A. J. Fridrich, B. D. Soukal, and A. J. Luk, “Detection of copy-move forgery in digital images,” in in Proceedings of Digital Forensic Research Workshop, 2003.Google Scholar
  2. 2.
    D. Vaishnavi and T. Subashini, “Image Tamper Detection Based on Edge Image and Chaotic Arnold Map,” Indian Journal of Science and Technology, vol. 8, no. 6, pp. 548–555, 2015.CrossRefGoogle Scholar
  3. 3.
    D. Vaishnavi and T. Subashini, “Fragile Watermarking Scheme Based on Wavelet Edge Features,” Journal of Electrical Engineering & Technology, vol. 10, no. 5, pp. 2149–2154, 2015.CrossRefGoogle Scholar
  4. 4.
    F. Yang, J. Li, W. Lu, and J. Weng, “Copy-move forgery detection based on hybrid features,” Engineering Applications of Artificial Intelligence, vol. 59, pp. 73–83, 2017.CrossRefGoogle Scholar
  5. 5.
    M. Puri and V. Chopra, “A survey: Copy-Move forgery detection methods,” International journal of computer systems, vol. 3, 2016.Google Scholar
  6. 6.
    X. Pan and S. Lyu, “Region duplication detection using image feature matching,” Information Forensics and Security, IEEE Transactions on, vol. 5, no. 4, pp. 857–867, 2010.CrossRefGoogle Scholar
  7. 7.
    I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, and G. Serra, “A sift-based forensic method for copy–move attack detection and transformation recovery,” Information Forensics and Security, IEEE Transactions on, vol. 6, no. 3, pp. 1099–1110, 2011.CrossRefGoogle Scholar
  8. 8.
    I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, L. Del Tongo, and G. Serra, “Copy-move forgery detection and localization by means of robust clustering with J-linkage,” Signal Processing: Image Communication, vol. 28, no. 6, pp. 659–669, 2013.Google Scholar
  9. 9.
    P. Kakar and N. Sudha, “Exposing postprocessed copy paste forgeries through transform invariant features,” Information Forensics and Security, IEEE Transactions on, vol. 7, no. 3, pp. 1018–1028, 2012.CrossRefGoogle Scholar
  10. 10.
    P. Mishra, N. Mishra, S. Sharma, and R. Patel, “Region Duplication Forgery Detection Technique Based on SURF and HAC,” The Scientific World Journal, vol. 2013, 2013.Google Scholar
  11. 11.
    Y. Zhu, X. Shen, and H. Chen, “Copy-move forgery detection based on scaled ORB,” Multimedia Tools and Applications, pp. 1–13, 2015.Google Scholar
  12. 12.
    J.-M. Guo, Y.-F. Liu, and Z.-J. Wu, “Duplication forgery detection using improved DAISY descriptor,” Expert Systems with Applications, vol. 40, no. 2, pp. 707–714, 2013.CrossRefGoogle Scholar
  13. 13.
    P. L. Jiming ZHENG, “Detection of Copy-move Forgery in Digital Image using DAISY Descriptor,” Journal of Computational Information Systems, vol. 10, pp. 9369–9377, 2014.Google Scholar
  14. 14.
    V. Anand, M. F. Hashmi, and A. G. Keskar, “A copy move forgery detection to overcome sustained attacks using dyadic wavelet transform and sift methods,” in Intelligent Information and Database Systems, Springer, 2014, pp. 530–542.Google Scholar
  15. 15.
    M. F. Hashmi, A. R. Hambarde, and A. G. Keskar, “Copy move forgery detection using DWT and SIFT features,” in Intelligent Systems Design and Applications (ISDA), 2013 13th International Conference on, 2013, pp. 188–193.Google Scholar
  16. 16.
    D. Vaishnavi and T. Subashini, “A passive technique for image forgery detection using contrast context histogram features,” International Journal of Electronic Security and Digital Forensics, vol. 7, no. 3, pp. 278–289, 2015.CrossRefGoogle Scholar
  17. 17.
    A. S. Alfraih, J. A. Briffa, and S. Wesemeyer, “Cloning localization based on feature extraction and k-means clustering,” in Digital-Forensics and Watermarking, Springer, 2014, pp. 410–419.Google Scholar
  18. 18.
    J. Weickert, B. T. H. Romeny, and M. A. Viergever, “Efficient and reliable schemes for nonlinear diffusion filtering,” IEEE transactions on image processing, vol. 7, no. 3, pp. 398–410, 1998.CrossRefGoogle Scholar
  19. 19.
    P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on pattern analysis and machine intelligence, vol. 12, no. 7, pp. 629–639, 1990.CrossRefGoogle Scholar
  20. 20.
    M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM, vol. 24, no. 6, pp. 381–395, 1981.MathSciNetCrossRefGoogle Scholar
  21. 21.
    G. Muhammad, M. Hussain, and G. Bebis, “Passive copy move image forgery detection using undecimated dyadic wavelet transform,” Digital Investigation, vol. 9, no. 1, pp. 49–57, 2012.CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of CSEVardhaman College of EngineeringHyderabad, TelanganaIndia
  2. 2.Department of ITCVR College of EngineeringHyderabad, TelanganaIndia
  3. 3.Department of ITA.V.C. College of EngineeringMayiladuthurai, NagapattinamIndia

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