Detection of Duplication Forgery in Digital Images in Uniform and Non-uniform Regions

In this paper we propose a robust and fully automatic method to detect duplicated regions in digital images. Copied areas in uniform and non-uniform regions are detectable in our method. There are several methods to make forged images, but the most common is copy-move forgery, that the forger copies a part(s) of image and pasted it into another part(s) of that image. Many researchers have done beneficial researches on it. Most of them can find only copy-moved forgery. In other words, they can find regions which are only copied and pasted without any changes, but failed to find copied regions with scaling or rotating before pasting. Many forgers make some changes on copied regions so that the image sounds more natural. SIFT features can find forged regions even if they are rotated or scaled. This method has some other advantages, but failed to find flat copied regions. Zernike moments are invariant against rotation. They can find flat copied regions too, but sensitive to scaling. So it is clear that using these two features is very proper to detect all copied regions in an image. By applying SIFT detection method on overall the image and then Zernike moments detection method on regions where SIFT features have not been found, The processing time is reduced in comparison to applying each of these two methods on entire image.