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Image-into-Image Steganography Using Deep Convolutional Network

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Advances in Multimedia Information Processing – PCM 2018 (PCM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11165))

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Abstract

Raising payload capacity in image steganography without losing too much safety is a challenging task. This paper combines recent deep convolutional neural network methods with image-into-image steganography. We show that with the proposed method, the capacity can go up to 23.57 bpp (bits per pixel) by changing only 0.76% of the cover image. We applied several traditional steganography analysis algorithms and found out that the proposed method is quite robust.

The source code is available at: https://github.com/adamcavendish/Deep-Image-Steganography.

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Author information

Authors and Affiliations

  1. School of Computer Engineering Science, Shanghai University, Shanghai, China

    Pin Wu, Yang Yang & Xiaoqiang Li

  2. Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China

    Xiaoqiang Li

Authors
  1. Pin Wu
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  2. Yang Yang
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  3. Xiaoqiang Li
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Corresponding author

Correspondence to Xiaoqiang Li .

Editor information

Editors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Richang Hong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. University of Tokyo, Tokyo, Japan

    Toshihiko Yamasaki

  4. Hefei University of Technology, Hefei, China

    Meng Wang

  5. City University of Hong Kong, Hong Kong, Hong Kong

    Chong-Wah Ngo

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Wu, P., Yang, Y., Li, X. (2018). Image-into-Image Steganography Using Deep Convolutional Network. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_73

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  • DOI: https://doi.org/10.1007/978-3-030-00767-6_73

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00766-9

  • Online ISBN: 978-3-030-00767-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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