An Implementation of Copy-Move Forgery Detector using SIFT-based Feature Refinement

Abstract

This paper deals with refining the SIFT-based feature set for the Copy-Move forgery detection in image forensics. Many researchers attempt to extract the feature with advanced performance; however, high-dimensional feature sets increment the calculation cost and the detection time. For this reason, we intend to design a simple feature yet have good copy-move forgery detection performance. In addition to that, for various conditions of images, we extract the refined SIFT-based features of robust yet straightforward features applied to Copy-Move image forensics using only the SIFT features that have excellent invariance maintenance. In a Copy-Move forgery image, a definition has a similar distance and slope between a copy and move area. This paper proposed a new scheme for a Copy-Move forgery detection to satisfy the purpose. As a result, the Copy-Move area is detected with 97.8% upper. Thus, the detection ratio is confirmed as an