International Conference on Applied Machine Learning (ICAML)

Quantum Image Information Hiding Based on Genetic Algorithm

Peng Peng, Wenjian Gu, Jiajie Min, Xin Ouyang, Gaofeng Luo

Published Jul 1, 2022• 1 citation

Abstract

Information hiding based on quantum images mostly adopts the least significant qubit (LSQb) algorithm, which is constrained by the performance indexes of the quality of vision and capacity of imbedding. To meet the requirements of the quality of vision, capacity of imbedding, and the security of the embedded information, this paper proposes a data hiding method for quantum image based on genetic algorithm. Firstly, an enhanced quantum model of digital images is used to represent the image, and a chaotic method is used to encrypt and disrupt the image to be hidden. By designing quantum operations such as selection, crossover and mutation, the secret image is embedded into the relevant qubit plane of the quantum cover image. Finally, the quantum secret image is extracted through the quantum line inverse operation. Simulation results show that the quantum image information hiding method based on genetic algorithm not only enhances the embedding security, but also improves the PSNR by about 9% compared to the LSQb method with the same embedding capacity. The research results have theoretical significance and practical value for research related to quantum image information security and protection.

DOI: 10.1109/icaml57167.2022.00011

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