Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods. While resampling detection algorithms are effective in detecting splicing and resampling, copy-move detection algorithms excel in detecting cloning and region removal. In this paper, we combine these complementary approaches in a way that boosts the overall accuracy of image manipulation detection. We use the copy-move detection method as a pre-filtering step and pass those images that are classified as untampered to a deep learning based resampling detection framework. Experimental results on various datasets including the 2017 NIST Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and tampered images shows that there is a consistent increase of 8%-10% in detection rates, when copy-move algorithm is combined with different resampling detection algorithms.
@article{arxiv.1802.03154,
title = {Boosting Image Forgery Detection using Resampling Features and Copy-move analysis},
author = {Tajuddin Manhar Mohammed and Jason Bunk and Lakshmanan Nataraj and Jawadul H. Bappy and Arjuna Flenner and B. S. Manjunath and Shivkumar Chandrasekaran and Amit K. Roy-Chowdhury and Lawrence Peterson},
journal= {arXiv preprint arXiv:1802.03154},
year = {2018}
}