Localisation of spliced region using pixel correlation in digital images

Image forgery has become the main concern of the society over the past many years due to an increase in the number of false cases of image manipulation leaving a severe impact on the life of a victim. Therefore, it has become necessary to solidly build forgery detection method to overcome image forgery problem. This necessity led to research in this field of image processing and provide detection techniques to check the integrity of images. In this paper, a method for localization of such tampered region is proposed. The proposed method uses unsupervised learning approach to localize the region of image and find whether the region is forged or not. It uses GLCM, a textural feature descriptor that uses pixel correlation in image to extract features from it. Further, for feature matching, Euclidean and Hellinger distance is used and finally, the localization of tampered region is performed. Euclidean and Hellinger distance is calculated separately on each image for comparison of the results. The experimental results showed that Hellinger outperformed Euclidean distance in feature matching and gave better localization results.