RDLNN-based Image Forgery Detection and Forged Region Detection Using MOT

Abstract

Image forgery detection TEMPhas become an emerging research area due to the increasing number of forged images circulating on the internet and other social media, which leads to legal and social issues. Image forgery detection includes the classification of an image as forged or authentic and as well as localizing the forgery wifin the image. In this paper, we propose a Regression Deep Learning Neural Network (RDLNN) based image forgery detection followed by Modified Otsu Thresholding (MOT) algorithm to detect the forged region. The proposed model comprises five steps that are preprocessing, image decomposition, feature extraction, classification and block matching. In the preprocessing step, the RGB images are converted to YCbCr color format. Then, the images are decomposed using the new Polar Dyadic Wavelet Transform (PDyWT), followed by the extraction of important features. The classification phase called RDLNN effectively classifies the normal image and the forged image. For localization of the forgery, the forged image is divided into a number of blocks, and then Genetic Three Step Search (GTSS) algorithm is exploited to identify the dissimilar blocks. To get the exact forged region in the image, the dissimilar blocks are analyzed by the Modified Otsu Thresholding (MOT) algorithm. The proposed algorithm is compared wif widely used image forgery detection algorithms. The results show that the proposed method improves the forgery detection accuracy and precision by at least 6.04% and 3.77%, respectively, as compared to the already existent techniques such as ANFIS, KNN, ANN, and SVM. Moreover, the training time of the proposed network is lower by at least 64.3 % than the above existing techniques