Characterization of splicing in digital images using gray scale co-occurrence matrices

Image forgery has become the main concern of the society over the past years due to an increase in the number of fraudulent image manipulations. Therefore, it has become a necessity to build an effective forgery detection method to check the integrity and authenticity of images. In this paper, an unsupervised method for classification of forged images and a supervised method for localization of forged regions is proposed. The methods use 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. In another approach, a bounding box is taken from the image to get the testing area and the training area. SVM classifier is used for classification of forged blocks and the authentic blocks in the image. The experimentation is performed on CASIA v1.0 dataset.