A New Copy Move Forgery Detection Method Resistant to Object Removal with Uniform Background Forgery

Abstract EN

Users transfer large number of images everyday over the Internet.

Easy to use commercial and open source image editing tools have made intactness of images questionable.

Passive methods have been proposed in the literature to determine authenticity of images.

However, a specific type of forgery called “Object Removal with uniform Background forgery” becomes a problem for keypoint based methods in the literature.

In this paper, we proposed an effective copy move forgery detection technique.

The method uses AKAZE features and nonlinear scale space for detection of copied/pasted regions.

The proposed method detects “Object Removal with uniform Background” and “Replication” types of forgeries with high precision compared to similar works.

Experimental results also indicate that the method yields better discriminative capability compared to others even if forged image has been rotated, blurred, AWGN added, or compressed by JPEG to hide clues of forgery.