Comparative Analysis of Different Keypoint Based Copy-Move Forgery Detection Methods

Copy-move forgery is the most commonly performed type of forgery. For copy-move forgery detection, block based and keypoint based methods are available. In this paper, keypoint based features are chosen as they are computationally less complex as compared to block based features. Four different keypoint based feature extraction algorithms i.e. SURF, KAZE, Harris corner points and BRISK are analyzed in order to check their efficiency for copy-move forgery detection. The method used involves four basic stages: Image pre-processing, interest point detector, feature vector description and feature matching. The results are compared on the basis of accuracy, fl-score and precision which are calculated using a threshold parameter for matching algorithm. It has been concluded that KAZE features give best results in all performance metrics and Harris corner points turn out to be unsuitable for copy move forgery detection due to the fact that Harris corner points are not scale invariant and detect only corners instead of edges.