Signal and Information Processing

Reversible Information Hiding Algorithm in Ciphertext Domain with Multiple Embedding Based on Block Classification

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  • 1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
    2. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200120, China

Received date: 2021-11-24

  Online published: 2023-03-29

Abstract

Insufficient utilization of image blocks in the existing reversible information hiding methods results in a low embedding capacity of secret information. In order to address this issue, this paper proposes a multiple embedding reversible information hiding algorithm based on block classification. First, the original image is encrypted with a stream cipher, and the encrypted image is further divided into multiple non-overlapping blocks. Subsequently, the Most Significant Bit (MSB) adaptive prediction algorithm is used to predict the first pixel and other pixels in each block, which is marked as a usable block or an unusable block. Finally, the available blocks are reconstructed and embedded with secret data, and the non-available blocks are re-embedded with the Median-Edge Detector (MED) prediction algorithm to realize the embedding of secret information. When the receiver receives the secret image, the secret information is extracted by the data-hiding key, and the original image is restored by the encryption key. Experimental results demonstrate that the proposed method can significantly improve the embedding capacity of secret information embedding, while keeping a high image restoration quality. The overall performance is superior to existing methods in terms of both embedding capacity and image restoration quality.

Cite this article

ZHANG Xiangyu, LI Fengyong, QIN Chuan . Reversible Information Hiding Algorithm in Ciphertext Domain with Multiple Embedding Based on Block Classification[J]. Journal of Applied Sciences, 2023 , 41(2) : 272 -283 . DOI: 10.3969/j.issn.0255-8297.2023.02.008

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