Error-free separable reversible data hiding in encrypted images using linear regression and prediction error map

2019 ◽  
Vol 78 (22) ◽  
pp. 31441-31465 ◽  
Author(s):  
Kaimeng Chen ◽  
Chin-Chen Chang
Keyword(s):  
Linear Regression ◽  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Encrypted Images

Prediction error expansion-based reversible data hiding in encrypted images with public key cryptosystem

2019 ◽  
Vol 13 (10) ◽  
pp. 1705-1713 ◽  
Author(s):  
Mohsin Shah ◽  
Weiming Zhang ◽  
Honggang Hu ◽  
Xiaojuan Dong ◽  
Nenghai Yu
Keyword(s):  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Public Key ◽  
Public Key Cryptosystem ◽  
Prediction Error Expansion ◽  
Error Expansion ◽  
Encrypted Images

scholarly journals High-Capacity Reversible Data Hiding in Encrypted Images Based on Hierarchical Quad-Tree Coding and Multi-MSB Prediction

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 664
Author(s):  
Ya Liu ◽  
Guangdong Feng ◽  
Chuan Qin ◽  
Haining Lu ◽  
Chin-Chen Chang
Keyword(s):  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Cloud Storage ◽  
Additional Data ◽  
State Of The Art ◽  
Data Extraction ◽  
High Capacity ◽  
Quad Tree ◽  
Encrypted Images

Nowadays, more and more researchers are interested in reversible data hiding in encrypted images (RDHEI), which can be applied in privacy protection and cloud storage. In this paper, a new RDHEI method on the basis of hierarchical quad-tree coding and multi-MSB (most significant bit) prediction is proposed. The content owner performs pixel prediction to obtain a prediction error image and explores the maximum embedding capacity of the prediction error image by hierarchical quad-tree coding before image encryption. According to the marked bits of vacated room capacity, the data hider can embed additional data into the room-vacated image without knowing the content of original image. Through the data hiding key and the encryption key, the legal receiver is able to conduct data extraction and image recovery separately. Experimental results show that the average embedding rates of the proposed method can separately reach 3.504 bpp (bits per pixel), 3.394 bpp, and 2.746 bpp on three well-known databases, BOSSBase, BOWS-2, and UCID, which are higher than some state-of-the-art methods.

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