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Journal of Electronic Imaging

Blind identification of image manipulation type using mixed statistical moments
Author(s): Bo Gyu Jeong; Yong Ho Moon; Il Kyu Eom
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Paper 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.

Paper Details

Date Published: 18 February 2015
PDF: 12 pages
J. Electron. Imag. 24(1) 013029 doi: 10.1117/1.JEI.24.1.013029
Published in: Journal of Electronic Imaging Volume 24, Issue 1
Show Author Affiliations
Bo Gyu Jeong, Pusan National Univ. (Republic of Korea)
Yong Ho Moon, Gyeongsang National Univ. (Republic of Korea)
Il Kyu Eom, Pusan National Univ. (Republic of Korea)

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