Image splicing detection using 2-D phase congruency and statistical moments of characteristic function

A new approach to efficient blind image splicing detection is proposed in this paper. Image splicing is the process of making a composite picture by cutting and joining two or more photographs. The spliced image may introduce a number of sharp transitions such as lines, edges and corners. Phase congruency has been known as a sensitive measure of these sharp transitions and hence been proposed as features for splicing detection. In addition to the phase information, the magnitude information is also used for splicing detection. Specifically, statistical moments of characteristic functions of wavelet subbands have been examined to catch the difference between the authentic images and spliced images. Consequently, the proposed scheme extracts image features from moments of wavelet characteristic functions and 2-D phase congruency for image splicing detection. The experiments have demonstrated that the proposed approach can achieve a higher detection rate as compared with the state-of-the-art.