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Coverless Steganography for Digital Images Based on a Generative Model

Xintao Duan1,*, Haoxian Song1, Chuan Qin2, Muhammad Khurram Khan3

1 Henan Normal University, Xinxiang, Henan 453007, China.
2 University of Shanghai for Science and Technology, Shanghai 200093, China.
3 Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh, Saudi Arabia.

* Corresponding Author: Xintao Duan. Email: email.

Computers, Materials & Continua 2018, 55(3), 483-493.


In this paper, we propose a novel coverless image steganographic scheme based on a generative model. In our scheme, the secret image is first fed to the generative model database, to generate a meaning-normal and independent image different from the secret image. The generated image is then transmitted to the receiver and fed to the generative model database to generate another image visually the same as the secret image. Thus, we only need to transmit the meaning-normal image which is not related to the secret image, and we can achieve the same effect as the transmission of the secret image. This is the first time to propose the coverless image information steganographic scheme based on generative model, compared with the traditional image steganography. The transmitted image is not embedded with any information of the secret image in this method, therefore, can effectively resist steganalysis tools. Experimental results show that our scheme has high capacity, security and reliability.


Cite This Article

APA Style
Duan, X., Song, H., Qin, C., , M.K.K. (2018). Coverless steganography for digital images based on a generative model. Computers, Materials & Continua, 55(3), 483-493.
Vancouver Style
Duan X, Song H, Qin C, MKK. Coverless steganography for digital images based on a generative model. Comput Mater Contin. 2018;55(3):483-493
IEEE Style
X. Duan, H. Song, C. Qin, and M.K.K. "Coverless Steganography for Digital Images Based on a Generative Model," Comput. Mater. Contin., vol. 55, no. 3, pp. 483-493. 2018.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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