Abstract

The embedding capacity of a histogram-based reversible data hiding technique is primarily determined by the peak height of the histogram. Recently, some studies have tried to embed data in the histogram of prediction errors by modifying the error values and have better embedding efficiency. However, these methods offer no selective embedment mechanism to exclude the positions where the modification in the embedding operation contributes no capacity but merely degrade the image quality. In this paper, a novel coding method for reversible data hiding is presented. A two-stage prediction algorithm that fully exploits the pixel correlations is employed to create more embeddable spaces, and a selective embedment mechanism is used to enhance the image quality. According to the experimental results, the proposed method achieved the highest payload while maintaining the lowest distortion for most standard test images, comparing to other existing histogram-shifting-based reversible data hiding techniques.

Highlights

  • Data hiding is a technique that embeds data into cover media by slightly modifying their content [1] and has been used in many applications, such as tamper detection [2], copyright protection [3], and finger printing [4]

  • The embedding distortion in many applications is small, the distorted cover media cannot be recovered to their original state [6, 7]

  • The secret data were generated by using a pseudorandom number generator (PRNG)

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Summary

Introduction

Data hiding is a technique that embeds data into cover media by slightly modifying their content [1] and has been used in many applications, such as tamper detection [2], copyright protection [3], and finger printing [4]. The embedding distortion in many applications is small, the distorted cover media cannot be recovered to their original state [6, 7]. Some applications, such as in medical or military usages, allow no permanent embedding distortion in order to preserve content fidelity. This demand has highlighted the needs of reversible data hiding and has drawn much attention in the recent years [8,9,10]

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