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Steganalysis of least significant bit matching using multi-order differences

Authors

  • Zhihua Xia,

    Corresponding author
    1. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, China
    2. School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, China
    • Correspondence: Zhihua Xia, School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China.

      E-mail: xia_zhihua@163.com

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  • Xinhui Wang,

    1. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, China
    2. School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, China
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  • Xingming Sun,

    1. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, China
    2. School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, China
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  • Baowei Wang

    1. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, China
    2. School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, China
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ABSTRACT

This paper presents a learning-based steganalysis/detection method to attack spatial domain least significant bit (LSB) matching steganography in grayscale images, which is the antetype of many sophisticated steganographic methods. We model the message embedded by LSB matching as the independent noise to the image, and theoretically prove that LSB matching smoothes the histogram of multi-order differences. Because of the dependency among neighboring pixels, histogram of low order differences can be approximated by Laplace distribution. The smoothness caused by LSB matching is especially apparent at the peak of the histogram. Consequently, the low order differences of image pixels are calculated. The co-occurrence matrix is utilized to model the differences with the small absolute value in order to extract features. Finally, support vector machine classifiers are trained with the features so as to identify a test image either an original or a stego image. The proposed method is evaluated by LSB matching and its improved version “Hugo”. In addition, the proposed method is compared with state-of-the-art steganalytic methods. The experimental results demonstrate the reliability of the new detector. Copyright © 2013 John Wiley & Sons, Ltd.

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