Separable Reversible Data Hiding in Encryption Image with Two-Tuples Coding

Abstract

Separable Reversible Data Hiding in Encryption Image (RDH-EI) has become widely used in clinical and military applications, social cloud and security surveillance in recent years, contributing significantly to preserving the privacy of digital images. Aiming to address the shortcomings of recent works that directed to achieve high embedding rate by compensating image quality, security, reversible and separable properties, we propose a two-tuples coding method by considering the intrinsic adjacent pixels characteristics of the carrier image, which have a high redundancy between high-order bits. Subsequently, we construct RDH-EI scheme by using high-order bits compression, low-order bits combination, vacancy filling, data embedding and pixel diffusion. Unlike the conventional RDH-EI practices, which have suffered from the deterioration of the original image while embedding additional data, the content owner in our scheme generates the embeddable space in advance, thus lessening the risk of image destruction on the data hider side. The experimental results indicate the effectiveness of our scheme. A ratio of 28.91% effectively compressed the carrier images, and the embedding rate increased to 1.753 bpp with a higher image quality, measured in the PSNR of 45.76 dB.

Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area

“…Nonetheless, generating a sizeable redundant space in the encrypted image is challenging because, in theory, an effective encryption process will completely destroy the correlation among image pixels. As noted in [31], this is the main reason for the low embedding rates of the VRAE, RRBE methods. Fortunately, [31] proposed an interesting two-tuples coding method to represent the carrier image which can reserve an ample redundancy space in the encrypted images for embedding extra data.…”

“…As noted in [31], this is the main reason for the low embedding rates of the VRAE, RRBE methods. Fortunately, [31] proposed an interesting two-tuples coding method to represent the carrier image which can reserve an ample redundancy space in the encrypted images for embedding extra data. That means, combining the two-tuples coding and our two-bit embedding method, we may increase the embedding capacity of RDH methods in the encryption domain also.…”

“…As described in Section 2, the abilities to survive steganalysis and hide data in the compression and the encryption domains are not considered in this writeup yet. According to the excellent and thorough classification of recently published 24 RDH-in-the-encryptedimage (RDH-EI)-related papers presented in [31], the existing RDH-EI schemes [31] and the references therein] can be classified into the following three categories: vacating room after encryption (VRAE), reserving room before encryption (RRBE), and vacating room by encryption (VRBE). Notice that, according to the illustrated frameworks Figure 1 in [31], traditional RDH approaches (including LSB modification, DE, HS, and the one presented in this work) can still play the pivotal role of data embedding besides various adopted encryption steps.…”

“…According to the excellent and thorough classification of recently published 24 RDH-in-the-encryptedimage (RDH-EI)-related papers presented in [31], the existing RDH-EI schemes [31] and the references therein] can be classified into the following three categories: vacating room after encryption (VRAE), reserving room before encryption (RRBE), and vacating room by encryption (VRBE). Notice that, according to the illustrated frameworks Figure 1 in [31], traditional RDH approaches (including LSB modification, DE, HS, and the one presented in this work) can still play the pivotal role of data embedding besides various adopted encryption steps. Intuitively, however, to realize data hiding in the encryption domain, we must encrypt the carrier image first and then find redundant space in the encrypted image to embed additional data.…”

An Ensemble Learning Approach for Reversible Data Hiding in Encrypted Images with Fibonacci Transform

“…RDH can be implemented on a natural image [12][13][14][15] or over an encrypted image [16][17][18]. Drawbacks of using natural images as the cover image include:…”