Duplicate matching and estimating features for detection of copy-move images forgery

TL;DR

This paper introduces an interactive, error-correcting method for copy-move forgery detection in images, optimizing matching and transform estimation steps through a cost function and iterative correction to improve accuracy and robustness.

Contribution

It proposes a novel interactive detection framework that jointly optimizes matching and transform estimation, reducing error propagation in copy-move forgery detection.

Findings

Effective in detecting forgeries with rotation and scaling

Robust against noise and post-processing effects

Achieves higher accuracy than existing methods

Abstract

Copy-move forgery is the most popular and simplest image manipulation method. In this type of forgery, an area from the image copied, then after post processing such as rotation and scaling, placed on the destination. The goal of Copy-move forgery is to hide or duplicate one or more objects in the image. Key-point based Copy-move forgery detection methods have five main steps: preprocessing, feature extraction, matching, transform estimation and post processing that matching and transform estimation have important effect on the detection. More over the error could happens in some steps due to the noise. The existing methods process these steps separately and in case of having an error in a step, this error could be propagated to the following steps and affects the detection. To solve the above mentioned problem, in this paper the steps of the detection system interact with each other and if an error happens in a step, following steps are trying to detect and solve it. We formulate this interaction by defining and optimizing a cost function. This function includes matching and transform estimation steps. Then in an iterative procedure the steps are executed and in case of detecting error, the error will be corrected. The efficiency of the proposed method analyzed in diverse cases such as pixel image precision level on the simple forgery images, robustness to the rotation and scaling, detecting professional forgery images and the precision of the transformation matrix. The results indicate the better efficiency of the proposed method.