Open Access

ARTICLE

Binary Image Steganalysis Based on Distortion Level Co-Occurrence Matrix

Junjia Chen¹, Wei Lu^1,2,*, Yuileong Yeung¹, Yingjie Xue¹, Xianjin Liu¹, Cong Lin^1,3, Yue Zhang⁴

1 School of Data and Computer Science, Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou 510006, China.
2 State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China.
3 Center for Faculty Development and Educational Technology, Guangdong University of Finance and Economics, Guangzhou 510320, China.
4 Department of Computer Science, College of Engineering and Computer Science, Eastern Lake Avenue, Orlando, FL, USA.

* Corresponding author: Wei Lu. Email: .

Computers, Materials & Continua 2018, 55(2), 201-211. https://doi.org/10.3970/cmc.2018.01781

Abstract

In recent years, binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security. In most state-of-the-art binary image steganographic schemes, they always find out the flippable pixels to minimize the embedding distortions. For this reason, the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial domain. However, the distortion maps can be calculated for cover and stego images and the difference between them is significant. In this paper, a novel binary image steganalytic scheme is proposed, which is based on distortion level co-occurrence matrix. The proposed scheme first generates the corresponding distortion maps for cover and stego images. Then the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego images. Finally, support vector machine, based on the gaussian kernel, is used to classify the features. Compared with the prior steganalytic methods, experimental results demonstrate that the proposed scheme can effectively detect stego images.

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Keywords

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