Low dimensional DCT and DWT feature based model for detection of image splicing and copy-move forgery

Digital images are being used as a prominent carrier of visual information in this age of digitization. Images become more and more omnipresent in everyday life. The images can be easily manipulated due to the accessibility of many internet tools and advanced software. Previously many techniques have been developed to authenticate the images. But all the previous techniques have high dimension of feature vectors. Here, a low dimensional DCT and DWT based features have been introduced to authenticate the images. In this work, we are dealing with both the passive forgery (splicing and copy-move) simultaneously. Features are extracted through image statistics and pixel correlation from DCT and DWT domain. Ensemble classifier has been selected for training and testing. The classifier classifies whether the given images are forged or authentic. Further, it also classifies the forgery in spliced or copy-move. If there is copy-move, the proposed work also perform the region detection using a novel key-point based method. The proposed model gives good detection accuracy and high generalization capability which is independent of image formats. Experimental results demonstrate the performance of proposed work against different post-processing operations like scaling, rotation, and Gaussian noise. Also, the comparative results against different existing methods show the effectiveness of the proposed model.