Copy-move forgery detection by improved SIFT K-means algorithm
by Kavita Rathi; Parvinder Singh
International Journal of Computational Vision and Robotics (IJCVR), Vol. 14, No. 4, 2024

Abstract: Copy-move forgery, a type of forgery in which it copies features from the same image, poses the toughest challenge in image forgery detection. Key-point-based CMFD techniques outperform the block-based CMFD techniques. SIFT is the mostly used key-point-based techniques. The present algorithm improves upon the SIFT algorithm with improvements in the various steps of the workflow by adding Laplace of Gaussian and multiplying it by the square of the Gaussian kernel to make it real scale invariant, applying double level filtering at feature extraction, and filtering by using g2NN and K-mean clustering. The results in the form of recall, precision, and F1 measure outperformed the state-of-art key-point-based CMFD techniques over multiple datasets.

Online publication date: Thu, 04-Jul-2024

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