  Image Forgery is a problem of image forensics and its detection can be
leveraged using Deep Learning. In this paper we present an approach for
identification of authentic and tampered images done using image editing tools
with Error Level Analysis and Convolutional Neural Network. The process is
performed on CASIA ITDE v2 dataset and trained for 50 and 100 epochs
respectively. The respective accuracies of the training and validation sets are
represented using graphs.
