Image Blind Forensics Using Artificial Neural Network
Zhen Zhang, Yukun Bian, Xijian Ping · 2008
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 increasingly easy and indiscoverable. To implement image splicing blind detection, this paper proposes a new splicing detection model. Image splicing detection can be treated as a two-class pattern recognition problem, which builds the model using moment features and some image quality metrics (IQMs) extracted from the given test image. Artificial neural network (ANN) is chosen as a classifier to train and test the given images. Experimental results demonstrate that the proposed approach has a high accuracy rate, and the network selected can work properly, proving that the ANN is effective and suitable for this model.