Boosting Image Forgery Detection using Resampling Features and Copy-move analysis

Tajuddin Manhar Mohammed; Jason Bunk; Lakshmanan Nataraj; Jawadul H. Bappy; Arjuna Flenner; B. S. Manjunath; Shivkumar Chandrasekaran; Amit K. Roy-Chowdhury; Lawrence Peterson

arXiv · 1802.03154

Boosting Image Forgery Detection using Resampling Features and Copy-move analysis

Tajuddin Manhar Mohammed·Jason Bunk·Lakshmanan Nataraj·Jawadul H. Bappy·Arjuna Flenner·B. S. Manjunath·Shivkumar Chandrasekaran·Amit K. Roy-Chowdhury·Lawrence Peterson

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Abstract

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.

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Tajuddin Manhar Mohammed, Jason Bunk, Lakshmanan Nataraj, Jawadul H. Bappy, Arjuna Flenner, B. S. Manjunath, Shivkumar Chandrasekaran, Amit K. Roy-Chowdhury, Lawrence Peterson. 2018-02-09. Boosting Image Forgery Detection using Resampling Features and Copy-move analysis. https://arxiv.org/abs/1802.03154

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