Publisher : The Korean Institute of Information and Commucation Engineering
DOI : 10.6109/jkiice.2016.20.4.833
Title & Authors
Selective Feature Extraction Method Between Markov Transition Probability and Co-occurrence Probability for Image Splicing Detection Han, Jong-Goo; Eom, Il-Kyu; Moon, Yong-Ho; Ha, Seok-Wun;
In this paper, we propose a selective feature extraction algorithm between Markov transition probability and co-occurrence probability for an effective image splicing detection. The Features used in our method are composed of the difference values between DCT coefficients in the adjacent blocks and the value of Kullback-Leibler divergence(KLD) is calculated to evaluate the differences between the distribution of original image features and spliced image features. KLD value is an efficient measure for selecting Markov feature or Co-occurrence feature because KLD shows non-similarity of the two distributions. After training the extracted feature vectors using the SVM classifier, we determine whether the presence of the image splicing forgery. To verify our algorithm we used grid search and 6-folds cross-validation. Based on the experimental results it shows that the proposed method has good detection performance with a limited number of features compared to conventional methods.
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