Discriminating Original Region from Duplicated One in Copy-Move Forgery

TL;DR

This paper proposes a novel method to distinguish original regions from duplicated ones in copy-move forgery images by analyzing border texture statistics, aiding automatic forgery detection.

Contribution

It introduces a new approach based on border texture analysis to discriminate original from duplicated regions in copy-move forgeries, addressing a gap in existing detection methods.

Findings

Effective discrimination between original and duplicated regions.

Validated method on realistic forged images from GRIP dataset.

Improves automatic forgery detection accuracy.

Abstract

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.