Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol. 9, Issue 5 , 2022
Copy- Move Forgery Detection Using Passive Technique

Author : U. Neha 1 K. Priyadharshini 2 M.Priyanka 3 K.Kiruthika 4

Date of Publication :7th March 2016

Abstract: An extensive growth in digital technology has resulted in tampering of image. Image plays an important role in today’s digital world. Powerful image editing tools has made it difficult to determine whether the image is authentic or forged. The main objective of this paper is to detect the forgery in which a part of the image is copied and pasted on to same image without the information of the original image. First, the forged image is segmented. With the help of SIFT features, key points are extracted. The matching process of these key points detects the copy move region.

Reference :

    1. Jian Li, Xiaolong Li, Bin Yang, and Xingming Sun, ” Segmentation-Based Image Copy-Move Forgery Detection Scheme,” in IEEE Trans on information forensics and security, vol. 10, no. 3, march 2015.
    2. Popescu A, Farid H. Exposing digital forgeries by detecting duplicated image regions. Technical Report TR2004-515. Department of Computer Science, Dartmouth College; 2004
    3. Fridrich, A. Jessica, B. David Soukal, and A. Jan Lukáš. "Detection of copy-move forgery in digital images." in Proceedings of Digital Forensic Research Workshop. 2003.
    4. Saiqa Khan, ArunKulkarni, “ obust Method for Detection of Copy-Move Forgery in Digital Images,” International Conference on Signal and Image Processing, 2010.
    5. S. Bayram,H. Sencar, and N.Memon, “An efficient and robust method for detecting copy-move forgery,” in Proc. IEEE Int. Conf. Acoustics,Speech, and Signal Processing, pp. 1053–1056, Apr. 2009.
    6. I. Amerini, L. Ballan, . Caldelli, A. D. Bimbo, and G. Serra, “A SIFT based forensic method for copy-move attack detection and transformation recovery,” IEEE Transactions on Information Forensics and Security, vol. 6, no. 3, pp. 1099–1110, Sep. 2011.
    7. Xu Bo, Wang Junwen, Liu Guangjie and Dai Yuewei, “Image Copy-move Forgery Detection Based on SU F,” International Conference on Multimedia Information Networking and Security,2010 .
    8. H. Farid, “Exposing digital forgeries in scientific images,” in Proc. 8th Workshop Multimedia Secur. (MM&Sec), New York, NY, USA, 2006,pp. 29–36.
    9. B. Liu, C.-M. Pun, and X.-C. Yuan, “Digital image forgery detection using JPEG features and local noise discrepancies,” Sci. World J., vol. 2014, pp. 1–12, Mar. 2014, Art. ID 230425.
    10. Ashraf A. Aly1, Safaai Bin Deris2, Nazar Zaki3,” Research Review For Digital Image Segmentation Techniques,” in International Journal of Computer Science & Information Technology (IJCSIT) Vol 3, No 5, Oct 2011
    11. Maja Rudinac, Boris Lenseigne, Pieter Jonker, “Keypoint extraction and selection for object recognition,” in MVA2009 IAPR Conference on Machine Vision Applications, May 20-22, 2009, Yokohama, JAPAN
    12. Huang, Hailing, Weiqiang Guo, and Yu Zhang. "Detection of copy-move forgery in digital images using SIFT algorithm." Computational Intelligence and Industrial Application, 2008. PACIIA'08. Pacific-Asia Workshop on. Vol. 2. IEEE, 2008.
    13. H. Bay, T. Tuytelaars, L. van Gool, “SURF: Speeded Up Robust Features,” Computer Vision and image understanding , Vol. 110,No. 3, pp. 346–359, 2008
    14. A Vedaldi and B. Fulkerson. (2008). VLFeat: An Open and Portable Library of Computer Vision Algorithms[online]. Available: http://www.vlfeat.org

Recent Article