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StegNet: Mega Image Steganography Capacity with Deep Convolutional Network

Author

Listed:
  • Pin Wu

    (School of Computer Science, Shanghai University, Shanghai 200444, China)

  • Yang Yang

    (School of Computer Science, Shanghai University, Shanghai 200444, China)

  • Xiaoqiang Li

    (School of Computer Science, Shanghai University, Shanghai 200444, China
    Shanghai Institute for Advanced Communication & Data Science, Shanghai University, Shanghai 200444, China)

Abstract

Traditional image steganography often leans interests towards safely embedding hidden information into cover images with payload capacity almost neglected. This paper combines recent deep convolutional neural network methods with image-into-image steganography. It successfully hides the same size images with a decoding rate of 98.2% or bpp (bits per pixel) of 23.57 by changing only 0.76% of the cover image on average. Our method directly learns end-to-end mappings between the cover image and the embedded image and between the hidden image and the decoded image. We further show that our embedded image, while with mega payload capacity, is still robust to statistical analysis.

Suggested Citation

  • Pin Wu & Yang Yang & Xiaoqiang Li, 2018. "StegNet: Mega Image Steganography Capacity with Deep Convolutional Network," Future Internet, MDPI, vol. 10(6), pages 1-15, June.
    • Handle: RePEc:gam:jftint:v:10:y:2018:i:6:p:54-:d:152721
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    Cited by:

    1. Jiaohua Qin & Jing Wang & Yun Tan & Huajun Huang & Xuyu Xiang & Zhibin He, 2020. "Coverless Image Steganography Based on Generative Adversarial Network," Mathematics, MDPI, vol. 8(9), pages 1-11, August.

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