Open Access
ARTICLE
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. https://doi.org/10.32604/cmc.2020.09723
Received 16 January 2020; Accepted 24 April 2020; Issue published 16 September 2020
Abstract
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.
Keywords
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. https://doi.org/10.32604/cmc.2020.09723
Citations