Optimizing least-significant-bit substitution using cat swarm optimization strategy

Embedding secret data into a cover image using simple least-significant-bit substitution can degrade the image quality dramatically, especially when a large number of bits are substituted. The exhaustive least-significant-bit substitution method is proposed to solve this problem. However, the idea has no practical application due to its long computation time. This paper adopts the cat swarm optimization (CSO) strategy to obtain the optimal or near optimal solution of the stego-image quality problem. The CSO strategy is generated by observing the behavior of cats, which has been proved to achieve better performance on finding the best global solutions. We revised the CSO strategy in our proposed scheme to make it practicable and suitable to solve the mentioned problem. The experimental results show that the proposed scheme can obtain a better solution with less computation time.

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