High-capacity reversible data hiding in encrypted images by prediction error

2014 ◽  
Vol 104 ◽  
pp. 387-400 ◽  
Author(s):  
Xiaotian Wu ◽  
Wei Sun
Keyword(s):  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
High Capacity ◽  
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.

scholarly journals High-Capacity Reversible Data Hiding in Encrypted Images by Information Preprocessing

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xi-Yan Li ◽  
Xia-Bing Zhou ◽  
Qing-Lei Zhou ◽  
Shi-Jing Han ◽  
Zheng Liu
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
High Capacity ◽  
Main Idea ◽  
Original Image ◽  
Chaos Encryption ◽  
Secret Information ◽  
Encrypted Image ◽  
Encrypted Images ◽  
Scanned Documents

With the development of cloud computing, high-capacity reversible data hiding in an encrypted image (RDHEI) has attracted increasing attention. The main idea of RDHEI is that an image owner encrypts a cover image, and then a data hider embeds secret information in the encrypted image. With the information hiding key, a receiver can extract the embedded data from the hidden image; with the encryption key, the receiver reconstructs the original image. In this paper, we can embed data in the form of random bits or scanned documents. The proposed method takes full advantage of the spatial correlation in the original images to vacate the room for embedding information before image encryption. By jointly using Sudoku and Arnold chaos encryption, the encrypted images retain the vacated room. Before the data hiding phase, the secret information is preprocessed by a halftone, quadtree, and S-BOX transformation. The experimental results prove that the proposed method not only realizes high-capacity reversible data hiding in encrypted images but also reconstructs the original image completely.

A high-capacity reversible data hiding method for homomorphic encrypted images

2019 ◽  
Vol 62 ◽  
pp. 87-96 ◽  
Author(s):  
Hao-Tian Wu ◽  
Yiu-ming Cheung ◽  
Zhiyuan Yang ◽  
Shaohua Tang
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
High Capacity ◽  
Encrypted Images

scholarly journals High-Capacity Reversible Data Hiding in Encrypted Images by Bit Plane Partition and MSB Prediction

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 62361-62371 ◽  
Author(s):  
Hao-Tian Wu ◽  
Zhiyuan Yang ◽  
Yiu-Ming Cheung ◽  
Lingling Xu ◽  
Shaohua Tang
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
High Capacity ◽  
Plane Partition ◽  
Bit Plane ◽  
Encrypted Images

Reversible data hiding in encrypted images with high capacity by bitplane operations and adaptive embedding

2017 ◽  
Vol 77 (16) ◽  
pp. 20917-20935 ◽  
Author(s):  
Fuqiang Di ◽  
Fangjun Huang ◽  
Minqing Zhang ◽  
Jia Liu ◽  
Xiaoyuan Yang
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
High Capacity ◽  
Adaptive Embedding ◽  
Encrypted Images

High Capacity Reversible Data Hiding in Encrypted Images by Patch-Level Sparse Representation

2016 ◽  
Vol 46 (5) ◽  
pp. 1132-1143 ◽  
Author(s):  
Xiaochun Cao ◽  
Ling Du ◽  
Xingxing Wei ◽  
Dan Meng ◽  
Xiaojie Guo
Keyword(s):  
Sparse Representation ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
High Capacity ◽  
Encrypted Images

High capacity reversible data hiding scheme for encrypted images

2016 ◽  
Vol 44 ◽  
pp. 84-91 ◽  
Author(s):  
Thai-Son Nguyen ◽  
Chin-Chen Chang ◽  
Wen-Chi Chang
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
High Capacity ◽  
Encrypted Images

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

Computers ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 152
Author(s):  
Ching-Yu Yang ◽  
Ja-Ling Wu
Keyword(s):  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Medical Images ◽  
High Capacity ◽  
Personal Privacy ◽  
Hiding Capacity ◽  
X Ray ◽  
Magnetic Resonance Imaging Mri ◽  
Hidden Data

During medical treatment, personal privacy is involved and must be protected. Healthcare institutions have to keep medical images or health information secret unless they have permission from the data owner to disclose them. Reversible data hiding (RDH) is a technique that embeds metadata into an image and can be recovered without any distortion after the hidden data have been extracted. This work aims to develop a fully reversible two-bit embedding RDH algorithm with a large hiding capacity for medical images. Medical images can be partitioned into regions of interest (ROI) and regions of noninterest (RONI). ROI is informative with semantic meanings essential for clinical applications and diagnosis and cannot tolerate subtle changes. Therefore, we utilize histogram shifting and prediction error to embed metadata into RONI. In addition, our embedding algorithm minimizes the side effect to ROI as much as possible. To verify the effectiveness of the proposed approach, we benchmarked three types of medical images in DICOM format, namely X-ray photography (X-ray), computer tomography (CT), and magnetic resonance imaging (MRI). Experimental results show that most of the hidden data have been embedded in RONI, and the performance achieves high capacity and leaves less visible distortion to ROIs.

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