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Analysis of Deep Learning-Based Image Steganalysis Methods Under Different Steganographic Algorithms

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Advances in Visual Computing (ISVC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13599))

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Abstract

In this paper, we present an experiment to evaluate the performance of deep-learning-based steganalysis methods under different steganographic algorithms and payloads. We designed and implemented a simple and efficient convolutional neural network (CNN) steganalysis model. This model combines SRM [10] filters and 2D Gabor filters to initialize the CNN’s filters in the feature extraction layers. The extracted features are passed to a fully connected neural network classifier to detect images containing hidden messages. We generated several datasets using different steganographic algorithms and payloads to train and test our CNN model. We designed several experiments to evaluate the detection rate of the proposed model under separate training-testing conditions.

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Authors and Affiliations

  1. Missouri State University, Springfield, MO, 65804, USA

    AlaaIdin Dwaik & Yassine Belkhouche

Authors
  1. AlaaIdin Dwaik
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  2. Yassine Belkhouche
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Corresponding author

Correspondence to Yassine Belkhouche .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. University of Illinois Urbana-Champaign, Urbana, IL, USA

    Bo Li

  3. National University of Singapore, Singapore, Singapore

    Angela Yao

  4. Microsoft Research Asia, Beijing, China

    Yang Liu

  5. University of Missouri, Columbia, MO, USA

    Ye Duan

  6. City University of Hong Kong, Kowloon, Hong Kong

    Manfred Lau

  7. Idaho National Laboratory, Idaho Falls, ID, USA

    Rajiv Khadka

  8. Salesforce, Seattle, WA, USA

    Ana Crisan

  9. Tufts University, Medford, MA, USA

    Remco Chang

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Dwaik, A., Belkhouche, Y. (2022). Analysis of Deep Learning-Based Image Steganalysis Methods Under Different Steganographic Algorithms. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2022. Lecture Notes in Computer Science, vol 13599. Springer, Cham. https://doi.org/10.1007/978-3-031-20716-7_22

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  • DOI: https://doi.org/10.1007/978-3-031-20716-7_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20715-0

  • Online ISBN: 978-3-031-20716-7

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