Advanced Software Techniques for Detecting Digital Image Manipulation

It is necessary to detect forgery of digital images in order to maintain their integrity. In this paper an attempt is made to solve the problem of copy-move forgery detection which is the most common form of image manipulation. We propose two new methods for detecting duplicated regions which are based on the texture and statistical features. The first technique is based on the classical SIFT (Scale-Invariant Feature Transform) which is a keypoint based method; the second one is based on the integration of SIFT into deep learning techniques and software solutions. We used the MICC-F600 database to evaluate the proposed methods and split it into training, validation and test sets. To increase performance of the model, some pre-processing steps were included such as scaling, image polishing, and so on. Based on the experimental results, our software-embedded convolutional neural network (CNN) models reached the greatest accuracy in identifying forged images. The model develops a binary mask that estimates the location of forgery in an image, which assists to improve the precision of digital forensic examination with our intelligent software.