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StegColNet: Steganalysis Based on an Ensemble Colorspace Approach

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Structural, Syntactic, and Statistical Pattern Recognition (S+SSPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12644))

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Abstract

Image steganography refers to the process of hiding information inside images. Steganalysis is the process of detecting a steganographic image. We introduce a steganalysis approach that uses an ensemble color space model to obtain a weighted concatenated feature activation map. The concatenated map helps to obtain certain features explicit to each color space. We use a levy-flight grey wolf optimization strategy to reduce the number of features selected in the map. We then use these features to classify the image into one of two classes: whether the given image has secret information stored or not. Extensive experiments have been done on a large scale dataset extracted from the Bossbase dataset. Also, we show that the model can be transferred to different datasets and perform extensive experiments on a mixture of datasets. Our results show that the proposed approach outperforms the recent state of the art deep learning steganalytical approaches by 2.32% on average for 0.2 bits per channel (bpc) and 1.87% on average for 0.4 bpc.

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Author information

Authors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Shreyank N. Gowda

  2. Tsinghua University, Beijing, China

    Chun Yuan

Authors
  1. Shreyank N. Gowda
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  2. Chun Yuan
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Editor information

Editors and Affiliations

  1. Ca’ Foscari University of Venice, Venice, Italy

    Andrea Torsello

  2. Queen Mary University of London, London, UK

    Luca Rossi

  3. Università Ca' Foscari Venezia, Venice, Italy

    Marcello Pelillo

  4. University of Cagliari, Cagliari, Italy

    Battista Biggio

  5. Deakin University, Burwood, VIC, Australia

    Antonio Robles-Kelly

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Gowda, S.N., Yuan, C. (2021). StegColNet: Steganalysis Based on an Ensemble Colorspace Approach. In: Torsello, A., Rossi, L., Pelillo, M., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2021. Lecture Notes in Computer Science(), vol 12644. Springer, Cham. https://doi.org/10.1007/978-3-030-73973-7_30

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  • DOI: https://doi.org/10.1007/978-3-030-73973-7_30

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  • Online ISBN: 978-3-030-73973-7

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