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Coverless Image Steganography Based on Generative Adversarial Network

Author

Listed:
  • Jiaohua Qin

    (College of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, China
    College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha 410004, China)

  • Jing Wang

    (College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha 410004, China)

  • Yun Tan

    (College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha 410004, China)

  • Huajun Huang

    (College of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, China)

  • Xuyu Xiang

    (College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha 410004, China)

  • Zhibin He

    (College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha 410004, China)

Abstract

Traditional image steganography needs to modify or be embedded into the cover image for transmitting secret messages. However, the distortion of the cover image can be easily detected by steganalysis tools which lead the leakage of the secret message. So coverless steganography has become a topic of research in recent years, which has the advantage of hiding secret messages without modification. But current coverless steganography still has problems such as low capacity and poor quality .To solve these problems, we use a generative adversarial network (GAN), an effective deep learning framework, to encode secret messages into the cover image and optimize the quality of the steganographic image by adversaring. Experiments show that our model not only achieves a payload of 2.36 bits per pixel, but also successfully escapes the detection of steganalysis tools.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1394-:d:401612
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    References listed on IDEAS

    as
    1. 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.
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    Cited by:

    1. Xishun Zhu & Zhengliang Lai & Nanrun Zhou & Jianhua Wu, 2022. "Steganography with High Reconstruction Robustness: Hiding of Encrypted Secret Images," Mathematics, MDPI, vol. 10(16), pages 1-19, August.

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