Table of Content

Open Access

ARTICLE

Reversible Data Hiding in Classification-Scrambling Encrypted-Image Based on Iterative Recovery

Yuyu Chen1, Bangxu Yin2, Hongjie He2, Shu Yan2, Fan Chen2,*, Hengming Tai3
Mao Yisheng Honors College, Southwest Jiaotong University, Chengdu , 611756, China.
School of Information Science and Technology, Southwest Jiao tong University, Chengdu, 611756, China.
Department of Electrical Engineering, University of Tulsa, Tulsa, OK 74104, USA.
* Corresponding Author: Fan Chen. Email: .

Computers, Materials & Continua 2018, 56(2), 299-312. https://doi.org/ 10.3970/cmc.2018.03179

Abstract

To improve the security and quality of decrypted images, this work proposes a reversible data hiding in encrypted image based on iterative recovery. The encrypted image is firstly generated by the pixel classification scrambling and bit-wise exclusive-OR (XOR), which improves the security of encrypted images. And then, a pixel-type-mark generation method based on block-compression is designed to reduce the extra burden of key management and transfer. At last, an iterative recovery strategy is proposed to optimize the marked decrypted image, which allows the original image to be obtained only using the encryption key. The proposed reversible data hiding scheme in encrypted image is not vulnerable to the ciphertext-only attack due to the fact that the XOR-encrypted pixels are scrambled in the corresponding encrypted image. Experimental results demonstrate that the decrypted images obtained by the proposed method are the same as the original ones, and the maximum embedding rate of proposed method is higher than the previously reported reversible data hiding methods in encrypted image.

Keywords

Reversible data hiding, image encryption, scrambling encryption, iterative recovery.

Cite This Article

Y. . Chen, B. . Yin, H. . He, S. . Yan, F. . Chen et al., "Reversible data hiding in classification-scrambling encrypted-image based on iterative recovery," Computers, Materials & Continua, vol. 56, no.2, pp. 299–312, 2018.



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.
  • 2102

    View

  • 1077

    Download

  • 0

    Like

Share Link

WeChat scan