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Blind identification of image manipulation type using mixed statistical moments

[+] Author Affiliations
Bo Gyu Jeong, Il Kyu Eom

Pusan National University, Department of Electronics Engineering, San 30, ChangJun, Kumjeong, Busan 609-735, Republic of Korea

Yong Ho Moon

Gyeongsang National University, Department of Aerospace and Software Engineering/ERI, 501, Jinju-daero, Gyeongnam 660-701, Republic of Korea

J. Electron. Imaging. 24(1), 013029 (Feb 18, 2015). doi:10.1117/1.JEI.24.1.013029
History: Received April 24, 2014; Accepted January 13, 2015
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Abstract.  We present a blind identification of image manipulation types such as blurring, scaling, sharpening, and histogram equalization. Motivated by the fact that image manipulations can change the frequency characteristics of an image, we introduce three types of feature vectors composed of statistical moments. The proposed statistical moments are generated from separated wavelet histograms, the characteristic functions of the wavelet variance, and the characteristic functions of the spatial image. Our method can solve the n-class classification problem. Through experimental simulations, we demonstrate that our proposed method can achieve high performance in manipulation type detection. The average rate of the correctly identified manipulation types is as high as 99.22%, using 10,800 test images and six manipulation types including the authentic image.

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Citation

Bo Gyu Jeong ; Yong Ho Moon and Il Kyu Eom
"Blind identification of image manipulation type using mixed statistical moments", J. Electron. Imaging. 24(1), 013029 (Feb 18, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.1.013029


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