Detection of Tool based Edited Images from Error Level Analysis and Convolutional Neural Network
This addresses image forensics for detecting tampered images, but it appears incremental as it applies existing methods to a specific dataset.
The paper tackles the problem of detecting tool-based edited images by combining Error Level Analysis with a Convolutional Neural Network, achieving results on the CASIA ITDE v2 dataset with accuracies reported via graphs for 50 and 100 epochs.
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.