High-fidelity reversible data hiding technique based on adaptive pairing of prediction errors

2022 ◽  
Author(s):  
Arun Kumar Rai ◽  
Hari Om ◽  
Satish Chand
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
High Fidelity ◽  
Prediction Errors

scholarly journals Reversible Data Hiding in Encrypted Images Using Median Edge Detector and Two’s Complement

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 921
Author(s):  
Rui Wang ◽  
Guohua Wu ◽  
Qiuhua Wang ◽  
Lifeng Yuan ◽  
Zhen Zhang ◽  
...  
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Personal Information ◽  
Rapid Development ◽  
Prediction Method ◽  
Prediction Errors ◽  
Edge Detector ◽  
Secret Data ◽  
Encrypted Images ◽  
Cloud Servers

With the rapid development of cloud storage, an increasing number of users store their images in the cloud. These images contain many business secrets or personal information, such as engineering design drawings and commercial contracts. Thus, users encrypt images before they are uploaded. However, cloud servers have to hide secret data in encrypted images to enable the retrieval and verification of massive encrypted images. To ensure that both the secret data and the original images can be extracted and recovered losslessly, researchers have proposed a method that is known as reversible data hiding in encrypted images (RDHEI). In this paper, a new RDHEI method using median edge detector (MED) and two’s complement is proposed. The MED prediction method is used to generate the predicted values of the original pixels and calculate the prediction errors. The adaptive-length two’s complement is used to encode the most prediction errors. To reserve room, the two’s complement is labeled in the pixels. To record the unlabeled pixels, a label map is generated and embedded into the image. After the image has been encrypted, it can be embedded with the data. The experimental results indicate that the proposed method can reach an average embedding rate of 2.58 bpp, 3.04 bpp, and 2.94 bpp on the three datasets, i.e., UCID, BOSSbase, BOWS-2, which outperforms the previous work.

scholarly journals A Location Map Reduced Reversible Data Hiding Method Using Prediction Error Modification

2012 ◽  
Vol 6-7 ◽  
pp. 428-433
Author(s):  
Yan Wei Li ◽  
Mei Chen Wu ◽  
Tung Shou Chen ◽  
Wien Hong
Keyword(s):  
Image Quality ◽  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Experimental Results ◽  
Cover Image ◽  
Prediction Errors ◽  
Secret Data ◽  
Location Map ◽  
Comparable Image Quality

We propose a reversible data hiding technique to improve Hong and Chen’s (2010) method. Hong and Chen divide the cover image into pixel group, and use reference pixels to predict other pixel values. Data are then embedded by modifying the prediction errors. However, when solving the overflow and underflow problems, they employ a location map to record the position of saturated pixels, and these pixels will not be used to carry data. In their method, if the image has a plenty of saturated pixels, the payload is decreased significantly because a lot of saturated pixels will not joint the embedment. We improve Hong and Chen’s method such that the saturated pixels can be used to carry data. The positions of these saturated pixels are then recorded in a location map, and the location map is embedded together with the secret data. The experimental results illustrate that the proposed method has better payload, will providing a comparable image quality.

High-fidelity reversible data hiding by Quadtree-based pixel value ordering

2018 ◽  
Vol 78 (6) ◽  
pp. 7125-7141 ◽  
Author(s):  
Fuqiang Di ◽  
Minqing Zhang ◽  
Xin Liao ◽  
Jia Liu
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
High Fidelity ◽  
Pixel Value

Improved PPVO-based high-fidelity reversible data hiding

2020 ◽  
Vol 167 ◽  
pp. 107264 ◽  
Author(s):  
Haorui Wu ◽  
Xiaolong Li ◽  
Yao Zhao ◽  
Rongrong Ni
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
High Fidelity
Sign in / Sign up

Export Citation Format

Share Document