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A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

Al-Hussien Seddik1, Mohammed Salah2, Gamal Behery2, Ahmed El-harby2, Ahmed Ismail Ebada2, Sokea Teng3, Yunyoung Nam3,*, Mohamed Abouhawwash4,5

1 Department of Computer Science, Faculty of Science, Minia University, Minia, Egypt
2 Department of Computer Science, Faculty of Computers and Artificial Intelligence, Damietta University, New Damietta, Egypt
3 Department of ICT Convergence, Soonchunhyang University, Asan, 31538, Korea
4 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
5 Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, 48824, MI, USA

* Corresponding Author: Yunyoung Nam. Email:

Computers, Materials & Continua 2023, 74(3), 5087-5103.


The ability of any steganography system to correctly retrieve the secret message is the primary criterion for measuring its efficiency. Recently, researchers have tried to generate a new natural image driven from only the secret message bits rather than using a cover to embed the secret message within it; this is called the stego image. This paper proposes a new secured coverless steganography system using a generative mathematical model based on semi Quick Response (QR) code and maze game image generation. This system consists of two components. The first component contains two processes, encryption process, and hiding process. The encryption process encrypts secret message bits in the form of a semi-QR code image whereas the hiding process conceals the pregenerated semi-QR code in the generated maze game image. On the other hand, the second component contains two processes, extraction and decryption, which are responsible for extracting the semi-QR code from the maze game image and then retrieving the original secret message from the extracted semi-QR code image, respectively. The results were obtained using the bit error rate (BER) metric. These results confirmed that the system achieved high hiding capacity, good performance, and a high level of robustness against attackers compared with other coverless steganography methods.


Cite This Article

A. Seddik, M. Salah, G. Behery, A. El-harby, A. I. Ebada et al., "A new generative mathematical model for coverless steganography system based on image generation," Computers, Materials & Continua, vol. 74, no.3, pp. 5087–5103, 2023.

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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