Re–compression Based JPEG Tamper Detection and Localization Using Deep Neural Network, Eliminating Compression Factor Dependency

Abstract

In this work, we deal with the problem of re–compression based image forgery detection, where some regions of an image are modified illegitimately, hence giving rise to presence of dual compression characteristics within a single image. There have been some significant researches in this direction, in the last decade. However, almost all existing techniques fail to detect this form of forgery, when the first compression factor is greater than the second. We address this problem in re–compression based forgery detection, here Recently, Machine Learning techniques have started gaining a lot of importance in the domain of digital image forensics. In this work, we propose a Convolution Neural Network based deep learning architecture, which is capable of detecting the presence of re–compression based forgery in JPEG images. The proposed architecture works equally efficiently, even in cases where the first compression ratio is greater than the second. In this work, we also aim to localize the regions of image manipulation based on re–compression features, using the trained neural network. Our experimental results prove that the proposed method outperforms the state–of–the–art, with respect to forgery detection and localization accuracy.