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Volume: 30 | Article ID: art00002
Boosting Image Forgery Detection using Resampling Features and Copy-move Analysis
  DOI :  10.2352/ISSN.2470-1173.2018.07.MWSF-118  Published OnlineJanuary 2018

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 A. Peterson, "Boosting Image Forgery Detection using Resampling Features and Copy-move Analysisin Proc. IS&T Int’l. Symp. on Electronic Imaging: Media Watermarking, Security, and Forensics,  2018,  pp 118-1 - 118-7,

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Electronic Imaging
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