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Symmetry 2018, 10(5), 128; https://doi.org/10.3390/sym10050128

Multi-Source Stego Detection with Low-Dimensional Textural Feature and Clustering Ensembles

1
College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, China
2
Computer Science Department, University of Victoria, Victoria, BC V8W 3P6, Canada
3
School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
Received: 1 March 2018 / Revised: 9 April 2018 / Accepted: 17 April 2018 / Published: 24 April 2018
(This article belongs to the Special Issue Information Technology and Its Applications 2018)
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Abstract

This work tackles a recent challenge in digital image processing: how to identify the steganographic images from a steganographer, who is unknown among multiple innocent actors. The method does not need a large number of samples to train classification model, and thus it is significantly different from the traditional steganalysis. The proposed scheme consists of textural features and clustering ensembles. Local ternary patterns (LTP) are employed to design low-dimensional textural features which are considered to be more sensitive to steganographic changes in texture regions of image. Furthermore, we use the extracted low-dimensional textural features to train a number of hierarchical clustering results, which are integrated as an ensemble based on the majority voting strategy. Finally, the ensemble is used to make optimal decision for suspected image. Extensive experiments show that the proposed scheme is effective and efficient and outperforms the state-of-the-art steganalysis methods with an average gain from 4 % to 6 % . View Full-Text
Keywords: multimedia security; steganalysis; steganographer detection; image texture feature; clustering ensembles multimedia security; steganalysis; steganographer detection; image texture feature; clustering ensembles
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Li, F.; Wu, K.; Zhang, X.; Lei, J.; Wen, M. Multi-Source Stego Detection with Low-Dimensional Textural Feature and Clustering Ensembles. Symmetry 2018, 10, 128.

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