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Reversible Data Hiding in Encrypted Images Based on Image Partition and Spatial Correlation

  • Chang SongEmail author
  • Yifeng Zhang
  • Guojun Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11378)

Abstract

Recently, more and more attention is paid to reversible data hiding (RDH) in encrypted images because of its better protection of privacy compared with traditional RDH methods directly operated in original images. In several RDH algorithms, prediction-error expansion (PEE) is proved to be superior to other methods in terms of embedding capacity and distortion of marked image and multiple histograms modification (MHM) can realize adaptive selection of expansion bins which depends on image content in the modification of a sequence of histograms. Therefore, in this paper, we propose an efficient RDH method in encrypted images by combining PEE and MHM, and design corresponding mode of image partition. We first divide the image into three parts: W (for embedding secret data), B (for embedding the least significant bit(LSB) of W) and G (for generating prediction-error histograms). Then, we apply PEE and MHM to embed the LSB of W to reserve space for secret data. Next, we encrypt the image and change the LSB of W to realize the embedding of secret data. In the process of extraction, the reversibility of image and secret data can be guaranteed. The utilization of correlation between neighbor pixels and embedded order decided by the smoothness of pixel in part W contribute to the performance of our method. Compared to the existing algorithms, experimental results show that the proposed method can reduce distortion to the image at given embedding capacity especially at low embedding capacity.

Keywords

Reversible data hiding Encrypted images Prediction-error expansion Multiple histograms modification 

Notes

Acknowledgments

This work was supported in part by the Natural Science Foundation of Jiangsu Province under Grant BK20151102, in part by the Ministry of Education Key Laboratory of Machine Perception, Peking University under Grant K-2016-03, in part by the Open Project Program of the Ministry of Education Key Laboratory of Underwater Acoustic Signal Processing, Southeast University under Grant UASP1502, and in part by the Natural Science Foundation of China under Grant 61673108.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Information Science and EngineeringSoutheast UniversityNanjingPeople’s Republic of China
  2. 2.Nanjing Institute of Communications TechnologiesNanjingPeople’s Republic of China
  3. 3.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China
  4. 4.Faculty of Science and TechnologyFederation University AustraliaMelbourneAustralia

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