Copy-Move Forgery Detection using SIFT and GLCM-based Texture Analysis

Easier access to editing tools and growing risk of image manipulation has encouraged extensive research in copy-move forgery detection. Although the current methods have been able to detect this tampering to a good extent, their accuracies drop when tested on images with different sizes of tampered regions and in the presence of similar but genuine objects in the image. In this paper, these issues are addressed by including a novel GLCM-based Texture Analysis Filter that gives information about the textural similarity of the keypoint neighbourhoods by using difference of GLCM contrasts as the similarity metric. Experimental results show that the proposed technique can address a variety of different tampering scenarios and outperforms the existing state-of-the-art Copy-Move Forgery Detection(CMFD) techniques by handling multiple forgeries, returning corresponding geometrical parameters and significantly improving the false positive rates.