Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. In the second method, resampling features computed on overlapping image patches are passed through a Long short-term memory (LSTM) based network for classification and localization. We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries.
@article{arxiv.1707.00433,
title = {Detection and Localization of Image Forgeries using Resampling Features and Deep Learning},
author = {Jason Bunk and Jawadul H. Bappy and Tajuddin Manhar Mohammed and Lakshmanan Nataraj and Arjuna Flenner and B. S. Manjunath and Shivkumar Chandrasekaran and Amit K. Roy-Chowdhury and Lawrence Peterson},
journal= {arXiv preprint arXiv:1707.00433},
year = {2017}
}