吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (2): 446-453.doi: 10.13229/j.cnki.jdxbgxb201402027

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Blind detection of image splicing based on visual attention model and extended hidden Markov model

SHEN Xuan-jing1,2, LI Xiang1,2, LYU Ying-da1,2, CHEN Hai-peng1,2   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
  • Received:2012-12-27 Online:2014-02-01 Published:2014-02-01

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Abstract:

In order to improve the detection accuracy of spliced images, a new blind detection based on the Visual Attention Model (VAM) was proposed in this study. First, the Edge Conspicuous Map (ECM) is created by an improved Order Statistics Filter (OSF) based nonlinear filtering approach; then, the ECM fixations are extracted by VAM, and the fixations on the boundaries are located by conspicuous edge positioning method, accordingly the key feature fragments are captured. Second, the Extended Hidden Markov Model (E-HMM) features are extracted from each wavelet reconstructed image of Cr channel of the fragments, and their dimensions are reduced by SVM-RFE. Finally, the above features are trained and classified using SVM, by which the spliced images can be identified from the natural ones. The experimental results show that, when testing on the Columbia image splicing detection dataset, the detection accuracy of the proposed method is 96.32%.

Key words: computer application, blind identification, image splicing, visual attention model, extended hidden Markov model

CLC Number: 

  • TP391

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