Deep-Learning-Based-Tamper-Detection-on-Images

Digital images are easy to manipulate and edit due to availability of powerful image processing and editing software. Nowadays, it is possible to add or remove important features from an image without leaving any obvious traces of tampering. As digital cameras and video cameras replace their analog counterparts, the need for authenticating digital images, validating their content, and detecting forgeries will only increase. Detection of malicious manipulation with digital images (digital forgeries) is the main topic of this project. Throughout the years, various computer vision and deep learning approaches have been pro- posed to solve the issue. In particular, a few of the CNN architectures suggested, manage to predict images with an accuracy of more than 96%. That said, the images used in those studies are easily recognized by humans. This raises a crucial question: how would CNNs perform on more challenging samples? In this study, we develop a CNN network inspired by a previous study and answer this question by analyzing various approaches to measure performance on CASIA2 and MICC datasets. We also measure the effect of a data augmentation technique and different hyper-parameters on classification performance.