IMAGE FORGERY DETECTION USING CNN MODEL

Abstract

Image forgery detection has become more relevant in the real world in recent years since it is so easy to change a particular image and share it throughout social media, which may quickly lead to fake news and fake rumors all over the world. These editing softwares have posed a significant challenge to image forensics in terms of proposing and implementing various methods and strategies for detecting image counterfeiting. There have been a variety of traditional approaches for forgery detection, but they all focus on simple feature extraction and are more specialized to the type of forgery. However, as research advances, multiple deep learning approaches are being implemented to identify forgeries in images. Deep learning approaches have demonstrated exceptional outcomes in image forgery when compared to traditional methods. The numerous sorts of image forgeries are discussed in this work. The work presents and compares different applied and proven image forgery detection approaches, as well as a comprehensive literature analysis of deep learning algorithms for detecting various types of image counterfeiting. Also CNN network is build based on a prior study and compare its performance on two different datasets to address this issue. Furthermore, the impact of a data augmentation approach is assessed as well as several hyperparameters on classification accuracy. Our findings imply that the dataset's difficulty has a significant influence on the outcomes. In this study, we have also aimed to determine detection of image forgery using deep learning approach. The CNN Model is used along with the ELA extraction model which is then used for detection of forgery in images. Later we also used two CNN Models, VGG16 Model and VGG19 Model for the better comparison and understanding.