Copy–Move Forgery Detection by Using Key-Point-Based Harris Features and CLA Clustering

Abstract

AbstractImages can easily be manipulated without any visual marks to the naked human eye with massive improvements in image manipulation software. This tampering is the main propelling force for the need of better image forensics such that field is known as image forgery detection. Any digital image with regions where the image contents are identical is said to have copy–move forgery (CMF). Copy–move forgery is performed to improve the visual features or to cover the underlying truth in the image. Many algorithms have been used for CMF detection, and this work is about improved key-point and clustering-based CMF detection scheme. The proposed scheme combines the efficiency of a key-point-based scheme and clustering of these key points to further improve the results. Modified Harris operator-based key-point detection algorithm with clustering using local gravitation is utilized for key-points selection. The average accuracy, PSNR and SSIM rates are used to evaluate the performance of the proposed algorithm with scale-invariant feature transform (SIFT), which is another state-of-the-art key-point algorithm. The paper concluded with the efficiency of the key-point-based scheme.KeywordsImage forensicsCopy–move image forgeryHarrisCLASIFTKey-point