Scale invariant fast PHT based Copy-Move forgery detection

Abstract

Copy-Move forgery is a type of image forgery wherein a patch from the image is copied and pasted on the same image either to increase the occurrence of a particular object or to conceal some important detail in the image. This paper addresses the issue of copy-move forgery using the block-based method of feature extraction. In block-based methods of feature extraction, PHT is one of the competing solutions, but it is not much robust to scaling. This paper proposes Scale-Invariant Fast PHT (SIFPHT) algorithm to detect the copy-move forgery which uses Fast PHT [1] for extracting the features from the blocks. Fast PHT has a higher convergence rate than the traditional PHT, and the results prove that the speed-up of almost 4 is attained for detecting the forgery. Moreover, the Fast PHT features so obtained from the blocks are normalized before comparison due to which the scaled forged segments are also identified. Further, Fast K-Means clustering is used to estimate the similarity in the blocks and hence detect the copy-move forgery.