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Unsupervised Optimization for Universal Spatial Image Steganalysis

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Abstract

Blind universal steganalysis has been the choice of Steganalysers owing to it’s capability to detect stego images without any prior information about the embedding method. Universal steganalysis is a two class optimization problem and the detecting efficiency depends on the feature set chosen from the stego and clean images. Though extracting all possible features of an image may lead to more efficiency the classification suffers due to large dimension of feature set. To overcome the problem of dimensionality appropriate feature reduction techniques need to be employed. This paper presents a blind universal image steganalysis technique that extracts the noise models of adjacent pixels of an image. The exact model construction involves the formation of four dimensional co-occurrence matrices of the quantised and truncated noise residues. From the 106 sub models 34,671 features have been extracted and further reduced by a novel unsupervised optimization technique to identify the most appropriate features for classification. The classifiers implemented include Support Vector Machines (SVM), Multi Layer Perceptron (MLP) and three fusion classifiers based on Bayes, Decision Template and Dempster Schafer fusion schemes. It has been identified that MLP performs better than SVM but is not superior to fusion classifiers. Comparing all the classifiers, Decision Template based fusion method gives the best classification accuracy (99.25%). Thus the proposed unsupervised optimization method combined with Decision Template fusion classification scheme provides the best classification of stego and clear images as compared to the existing research work.

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Authors and Affiliations

  1. Department of ECE, SRM University, Chennai, India

    Anita Christaline Johnvictor, Gomathy Chidambaram & Vaishali Durgamahanthi

  2. Saveetha Engineering College, Chennai, India

    Ramesh Rangaswamy

Authors
  1. Anita Christaline Johnvictor
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  2. Ramesh Rangaswamy
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  3. Gomathy Chidambaram
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  4. Vaishali Durgamahanthi
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Correspondence to Anita Christaline Johnvictor.

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Appendix

Appendix

See Figs. 6 and 7.

Fig. 6
figure 6

Image features extracted

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Fig. 7
figure 7

Image features extracted in .mat file

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Johnvictor, A.C., Rangaswamy, R., Chidambaram, G. et al. Unsupervised Optimization for Universal Spatial Image Steganalysis. Wireless Pers Commun 102, 1–18 (2018). https://doi.org/10.1007/s11277-018-5790-6

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  • DOI: https://doi.org/10.1007/s11277-018-5790-6

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