An Improved Reversible Data Hiding in Encrypted Images Using Parametric Binary Tree Labeling

Abstract

This work proposes an improved reversible data hiding scheme in encrypted images using parametric binary tree labeling(IPBTL-RDHEI), which takes advantage of the spatial correlation in the entire original image not in small image blocks to reserve room for embedding data before image encryption, then the original image is encrypted with a secret key and parametric binary tree labeling is used to label image pixels in two different categories. According to the experimental results, compared with several state-of-the-art methods, the proposed IPBTL-RDHEI method achieves higher embedding rate and outperforms the competitors. Due to the reversibility of IPBTL-RDHEI, the original content of the image and the secret information can be restored and extracted losslessly and separately.

Reversible Data Hiding in Encrypted Images Based on Multi-MSB Prediction and Huffman Coding

“…The key indicator of RDHEI is the size of EC, in other words, the ER. In order to better demonstrate the ER of our proposed method, this section compares ER with three latest state-of-the-art RDHEI methods [20,22,23]. As shown in Figure. 13, we tested five images using these methods.…”

“…For example, when using Puteaux et als method [20], the ER of Lena image is only 0.977 bpp. Compared with [20], Wu et al [23] considered that spatial correlation can be used to increase the ER, and finally the embedding rate of Lena image is 2.645 bpp. In addition, Yin et al [22] uses multiple MSBs, but still doesnt make use of the embeddable room of each bit plane.…”

“…However, the redundancy of the encrypted image is lower than that of the original image, which leads to the EC being unsatisfactory. To further improve EC, many RRBE methods have been proposed in the past few years [18][19][20][21][22][23][24]. Different from VRAE methods, these methods improve EC based on the spatial correlation of original images.…”

“…According to their results, the EC is higher than previous methods. In [23], Wu et al suggested taking advantage of the spatial correlation of original images and using parametric binary tree labeling to mark image pixels in two different categories. By using this scheme, original images can be recovered without loss and EC can also be improved.…”

“…Then, although Yin et al [22] take into account multiple MSBs, for each bit plane, the embeddable space is still not maximized, which also leads to their EC being unsatisfactory. Moreover, spatial correlation isn't fully utilized in Wu et al's scheme [23]. Considering the similarity between bit planes of gray images and binary images, that is, both bit planes and binary images are only composed of two types of values, we pay attention to Ren et al's scheme [24].…”