Blind identification of image manipulation type using mixed statistical moments

Bo Gyu Jeong; Yong Ho Moon; Il Kyu Eom

doi:10.1117/1.JEI.24.1.013029

18 February 2015 Blind identification of image manipulation type using mixed statistical moments

Bo Gyu Jeong, Yong Ho Moon, Il Kyu Eom

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 1, 013029 (February 2015). https://doi.org/10.1117/1.JEI.24.1.013029

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.

Citation Download Citation

Bo Gyu Jeong, Yong Ho Moon, and Il Kyu Eom "Blind identification of image manipulation type using mixed statistical moments," Journal of Electronic Imaging 24(1), 013029 (18 February 2015). https://doi.org/10.1117/1.JEI.24.1.013029

Published: 18 February 2015

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available