A Markov based image forgery detection approach by analyzing CFA artifacts

Abstract

The image acquisition device, the light is filtered through a Color Filter Array (CFA), where each pixel captures only one color (from Red, Green, and Blue), while others are calibrated. This process is known as interpolation process, and the artifacts introduced are called CFA or interpolation artifacts. The structure of these artifacts in the image is disturbed while a forgery is introduced in an image. In this paper, a high-order statistical approach is proposed to detect the inconsistencies in the artifacts of different parts of the image to expose any forgery present. The Markov Transition Probability Matrix (MTPM) is employed to develop various features that will detect the presence or absence of CFA artifacts in a particular region of the image. The Markov random process is applied because it provides an enhanced efficiency and reduced computational complexity for the forgery detection model. The algorithm is tested on 2 × 2 pixel block of the image which provides the results of a fine quality. There is no prior information of the location of the forged region of the image. The algorithm is tested on various images, taken from various social networking websites. The proposed forgery detection technique outperforms the existing state-of-the-art techniques for the different forgery scenarios by providing an average accuracy of 90.58%.