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Journal of Electronic Imaging

Efficient Markov feature extraction method for image splicing detection using maximization and threshold expansion
Author(s): Jong Goo Han; Tae Hee Park; Yong Ho Moon; Il Kyu Eom
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Paper Abstract

We propose an efficient Markov feature extraction method for color image splicing detection. The maximum value among the various directional difference values in the discrete cosine transform domain of three color channels is used to choose the Markov features. We show that the discriminability for slicing detection is increased through the maximization process from the point of view of the Kullback–Leibler divergence. In addition, we present a threshold expansion and Markov state decomposition algorithm. Threshold expansion reduces the information loss caused by the coefficient thresholding that is used to restrict the number of Markov features. To compensate the increased number of features due to the threshold expansion, we propose an even–odd Markov state decomposition algorithm. A fixed number of features, regardless of the difference directions, color channels and test datasets, are used in the proposed algorithm. We introduce three kinds of Markov feature vectors. The number of Markov features for splicing detection used in this paper is relatively small compared to the conventional methods, and our method does not require additional feature reduction algorithms. Through experimental simulations, we demonstrate that the proposed method achieves high performance in splicing detection.

Paper Details

Date Published: 29 April 2016
PDF: 8 pages
J. Electron. Imag. 25(2) 023031 doi: 10.1117/1.JEI.25.2.023031
Published in: Journal of Electronic Imaging Volume 25, Issue 2
Show Author Affiliations
Jong Goo Han, Pusan National Univ. (Korea, Republic of)
Tae Hee Park, Tongmyong Univ. (Republic of Korea)
Yong Ho Moon, Gyeongsang National Univ. (Korea, Republic of)
Il Kyu Eom, Pusan National Univ. (Korea, Republic of)

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