  Since images are used as evidence in many cases, validation of digital images
is essential. Copy-move forgery is a special kind of manipulation in which some
parts of an image is copied and pasted into another part of the same image.
Various methods have been proposed to detect copy-move forgery, which have
achieved promising results. In previous methods, a binary mask determining the
original and forged region is presented as the final result. However, it is not
specified which part of the mask is the forged region. It should be noted that
discriminating the original region from the duplicated one is not usually
feasible by human visual system(HVS). On the other hand, exact localizing the
forged region can be helpful for automatic forgery detection especially in
combined forgeries. In real-world forgeries, some manipulations are performed
in order to provide a visibly realistic scene. These modifications are usually
applied on the boundary of the duplicated snippets. In this research, the
texture information of the border regions of both the original and copied
patches have been statistically investigated. Based on this analysis, we
propose a method to discriminated copied snippets from original ones. In order
to validate our method, GRIP dataset is utilized since it contains more
realistic forged images which are not easily recognizable by HVS.
