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

Abstract

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.

Enhancing the Performance of Generative Adversarial Networks with Identity Blocks and Revised Loss Function to Improve Training Stability

“…Generative adversarial networks, which were introduced by Goodfellow [1], are at the vanguard of efforts to generate high-fidelity and diversified images. In recent years, models learned directly from data have significantly advanced the state of generative image modeling, including in biomedical imaging [2,3] and robotics [4,5]. The GAN network contains two parts: generator (G) and discriminator (D) networks.…”