Genetic algorithm and hamilton path based data hiding scheme including embedding cost optimization

Steganography is used at a large scale in various security systems. It is the science and art of hiding secret information into data. Various steganography schemes have been proposed over the years, but most of them are not promising enough to provide a large capacity of embedding and visually prevent the image's degradation. Histograms can reveal the existence of secret information, and it is also an essential issue in the security of the data. In this Paper, the main objective is to reduce the bit-flip cost count and maximize the PSNR value to reduce the image distortion and keep the data secure by using secret keys while embedding. In this proposed paper, the genetic algorithm (GA) is employed to select the best chromosome that has the minimum bit-flip cost count and maximum PSNR. Data security is achieved by the secret key generated from Hamiltonian path for embedding and retrieving of data. The proposed technique is robust against steganographic attacks and even if presence of data is observed it not possible to guess the embedding pattern. The result section demonstrates that the proposed technique outperform the existing techniques by increasing the PSNR significantly by approx 7 percent that lead to the increase in PSNR value up to 41.8dB for three bit per pixel embedding.

Automated summary

Steganography is widely used in security systems for hiding secret information into data. However, most existing schemes do not provide enough capacity for embedding and degrade the image quality. Histograms can reveal the presence of secret information and are important for data security. This paper proposes a technique that uses a genetic algorithm to select the best chromosome for embedding, reducing bit-flip cost count and maximizing PSNR value. The proposed technique is robust against steganographic attacks and significantly increases the PSNR by approximately 7%.