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International Conference on Intelligent Computing

ICIC 2020: Intelligent Computing Methodologies pp 611–623Cite as

Towards a Universal Steganalyser Using Convolutional Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12465))

Abstract

A universal steganalyser has been the goal of many research leading to some good trials. Such steganalysers relied on machine learning and a wide range of features that can be extracted from images. However, increasing the dimensionality of the extracted features leads to the rapid rise in the complexity of algorithms. In recent years, some studies have indicated that well-designed convolutional neural networks (CNN) can achieve comparable performance to the two-step machine learning approaches. This paper aims to investigate different CNN architectures and diverse training strategies to propose a universal steganalysis model that can detect the presence of secret data in a colour stego-image. Since the detection of a stego-image can be considered as a classification problem, a CNN-based classifier has been proposed here. The experimental results of the proposed approach proved the efficiency in the main aspects of image steganography compared with the current state-of-the-art methods. However, a universal steganalysis is still unachievable, and more work should be done in this field.

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Author information

Authors and Affiliations

  1. School of Electrical and Computer and Telecommunications Engineering, University of Wollongong, North Wollongong, NSW, 2522, Australia

    Inas Jawad Kadhim, Prashan Premaratne, Peter James Vial & Qasim Al-Shebani

  2. Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq

    Inas Jawad Kadhim

  3. Faculty of Information Technology, Monash University, Melbourne, Australia

    Osamah M. Al-Qershi

Authors
  1. Inas Jawad Kadhim
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  2. Prashan Premaratne
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  3. Peter James Vial
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  4. Osamah M. Al-Qershi
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  5. Qasim Al-Shebani
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Corresponding author

Correspondence to Inas Jawad Kadhim .

Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

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Kadhim, I.J., Premaratne, P., Vial, P.J., Al-Qershi, O.M., Al-Shebani, Q. (2020). Towards a Universal Steganalyser Using Convolutional Neural Networks. In: Huang, DS., Premaratne, P. (eds) Intelligent Computing Methodologies. ICIC 2020. Lecture Notes in Computer Science(), vol 12465. Springer, Cham. https://doi.org/10.1007/978-3-030-60796-8_53

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  • DOI: https://doi.org/10.1007/978-3-030-60796-8_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60795-1

  • Online ISBN: 978-3-030-60796-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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