A novel model for splicing detection

Abstract

With the advent of digital technology, digital image has gradually taken the place of the original analog photograph, and the forgery of digital image has become more and more easy and indiscoverable. Image splicing is a commonly used technique in image tampering. To implement image splicing detection a blind, passive and effective splicing detection scheme was proposed in this paper. Image splicing detection can be treated as a two-class pattern recognition problem, the model was based on moment features and some image quality metrics (IQMs) extracted from the given test image, which are sensitive to spliced image. Artificial neural network (ANN) is chosen as a classifier to train and test the given images. This model can measure statistical differences between original image and spliced image. Experimental results demonstrate that this new splicing detection algorithm is effective and reliable; indicating that the proposed approach has a broad application prospect.