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

IInternational Journal of Engineering Research in Mechanical and Civil Engineering (IJERMCE)

Monthly Journal for Mechanical and Civil Engineering

ISSN : 2456-1290 (Online)

Copy-Move Image Forgery Detection using Scale Invariant Feature Transform

Author : P. Parimala 1 Mrs. A. Naveena 2

Date of Publication :24th December 2020

Abstract: Abstract---Digital image forgery is a one of multimedia security whose objective is to show the wicked manipulations in digital images. Among different types of image forgery, copy–move forgery detection (CMFD) is the most popular one where a part of the original image is copied and pasted at another position in the same image. Various methods have been developed in the past few years. to achieve geometric transformation like rotation and scaling, a novel methodology based on Scale Invariant Features Transform (SIFT) is proposed. The proposed algorithm mainly involves in feature matching in which features are extracted from each block by computing the dot product between the unit vectors. Random Sample Consensus (RANSAC) algorithm is used to remove the false positive matches. The experimental results of the algorithm are presented to confirm that the technique can extract more accurate results compared with existing forgery detection methods.

Reference :

    1. merini, Irene, et al. “A SIFT-based forensic method for copy-move attack detection and transformation recovery”, Information Forensics and Security, IEEE Transactions on 6.3 (2011): 1099-1110.
    2. Amerini, Irene & Ballan, Lamberto & Caldelli, Roberto & Del Bimbo, A & Serra, Giuseppe. (2013). MICC-F220.
    3. Chi-Man Pun, Xiao-Chen Yuan and Xiu-Li Bi, “Image forgery detection using Adaptive over segmentation and feature point matching”, IEEE Trans.Inf. Forensics Security, vol. 10, Aug 2015
    4.  D. G. Lowe, “Object recognition from local scaleinvariant features”, in Proc. 7th IEEE Int. Conf. Comput. Vis., Sep. 1999, pp. 11501157.
    5.  Fridrich, A. Jessica, B. David Soukal, and A. Jan Lukas,” Detection of copy-move forgery in digital images”, In Proceedings of Digital Forensic Research Workshop, Cleveland, OH, USA, pp. 55-61, August 2003.
    6.  H. Huang, W. Guo, and Y. Zhang, “Detection of copy-move forgery in digital images using SIFT algorithm”, in Proc. Pacific-Asia Workshop Compute. Intell. Ind. Appl. (PACIIA), Dec. 2008, pp. 272276. .

Recent Article