Detecting Splicing and Copy-move Forgeries in Images Based on Convolutional Neural Network

Abstract

With the Internet development and the availability of image editing tools digital images can be easily manipulated and edited Therefore the credibility of digital images has faced severe challenges In digital image forensic the copy-move and splicing forgeries are popular forgery attacks For copy-move forgery a part of the image is copied and pasted elsewhere in the same image in order to cover possible important messages However the image splicing is to duplicate a region of another image to the original image so as to add the contents not belonging to the original image In this thesis a convolutional neural network (CNN) model is proposed to detect such tampering First the image is divided into fixed-size non-overlapping patches and the Radon transform is applied to each patch to compute the features After the network is trained the proposed model can classify the tampered and the authentic patches By classifying each patch in the images the duplicated regions can be detected The experimental results demonstrate that the accuracy of proposed method is better than other methods

Date of Award	2019
Original language	English
Supervisor	Shen-Chuan Tai (Supervisor)