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Reversible Data Hiding in Encrypted Images Based on Prediction and Adaptive Classification Scrambling

Lingfeng Qu1, Hongjie He1, Shanjun Zhang2, Fan Chen1, *

1 School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China.
2 Department of Information Science, The Faculty of Science, Kanagawa University, Kanagawa, 259129 Japan.

* Corresponding Author: Fan Chen. Email: .

Computers, Materials & Continua 2020, 65(3), 2623-2638.


Reversible data hiding in encrypted images (RDH-EI) technology is widely used in cloud storage for image privacy protection. In order to improve the embedding capacity of the RDH-EI algorithm and the security of the encrypted images, we proposed a reversible data hiding algorithm for encrypted images based on prediction and adaptive classification scrambling. First, the prediction error image is obtained by a novel prediction method before encryption. Then, the image pixel values are divided into two categories by the threshold range, which is selected adaptively according to the image content. Multiple high-significant bits of pixels within the threshold range are used for embedding data and pixel values outside the threshold range remain unchanged. The optimal threshold selected adaptively ensures the maximum embedding capacity of the algorithm. Moreover, the security of encrypted images can be improved by the combination of XOR encryption and classification scrambling encryption since the embedded data is independent of the pixel position. Experiment results demonstrate that the proposed method has higher embedding capacity compared with the current state-ofthe-art methods for images with different texture complexity.


Cite This Article

L. Qu, H. He, S. Zhang and F. Chen, "Reversible data hiding in encrypted images based on prediction and adaptive classification scrambling," Computers, Materials & Continua, vol. 65, no.3, pp. 2623–2638, 2020.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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