Image Forgery Authentication and Classification using Hybridization of HMM and SVM Classifier

Abstract

Image forgery is a major issue in today's digital publishing and printing. Now a day’s system can be used for forensic purpose to validate the authenticity of an image. In this paper we present an approach for image forgery authentication. We observe that a non morphed and non forged image shows homogeneity in non spectral domain. This homogeneity is lost when any forgery or morphing is applied on the images. We therefore apply a set of transform over the images. We combine DCT statistics, LBP features with curvelet statistics and Gabor transform of the images to represent an image in the transformed domain. CASIA image dataset with seven thousand authentic and same numbers of tempered images is used to verify the technique. We divide the dataset into equal halves to perform training and testing. Transformed images are used to train Hidden Markov model as HMM can extract probabilistic state information from a large statistical model. A test images is tested in transformed domain by HMM with log likelihood estimator. In case HMM returns an indeterminist result or multiple subset of result, the transformed test image is tested with two class SVM classifier with RBF kernel. Results show that the accuracy of the system is over 89% for 500 test instances.