Implementation of Block-Based Hierarchical Prediction for Developing an Error-Propagation-Free Reversible Data Hiding Scheme

This paper proposes a reversible data hiding technique based on the residual histogram shifting technique. To improve the hiding capacity, this study proposes a multiple-round hierarchical prediction mechanism that generates the prediction errors of each image block. The prediction errors of each block are collected to produce the residual histogram and the secret data are then embedded into the residual histogram to obtain the embedded image. Experimental results demonstrate that the proposed technique not only provides good hiding capacity, but also maintains good image quality of the embedded image. In addition, this technique can be easily extended for image integrity protection as it is capable of resisting error propagation.

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

Symmetry ◽

10.3390/sym13060921 ◽

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.

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

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.6-7.428 ◽

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.

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Block-Based Reversible Data Hiding Using Histogram Shifting and Modulus Operator for Digital Images

Journal of Circuits System and Computers ◽

10.1142/s0218126617501031 ◽

2017 ◽

Vol 26 (06) ◽

pp. 1750103 ◽

Cited By ~ 3

Author(s):

Pankaj Garg ◽

Singara Singh Kasana ◽

Geeta Kasana

Keyword(s):

Data Hiding ◽

Reversible Data Hiding ◽

Signal To Noise Ratio ◽

Cover Image ◽

Histogram Shifting ◽

Embedding Capacity ◽

Signal To Noise ◽

Secret Data ◽

Peak Points ◽

Block Based

A Reversible Data Hiding technique by using histogram shifting and modulus operator is proposed in which secret data is embedded into blocks of the cover image. These blocks are modified by using modulus operator to increase the number of peak points in the histogram of the cover image which further increases its embedding capacity. Secret data is embedded in the original cover blocks of the cover image by using peak points of the predicted blocks, which are generated by using modulus operator. Peak Signal to Noise Ratio and PSNR-Human Visual System are used to show the human visual acceptance of the proposed technique. Experimental results show that the embedding capacity is high as compared to the capacity of existing RDH techniques, while distortion in marked images is also less as compared to distortion produced by these existing techniques.

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Pixel Value Ordering Based Reversible Data Hiding with Novel MPBS Strategy

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b4248.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 1518-1524

Keyword(s):

Data Hiding ◽

Reversible Data Hiding ◽

Data Extraction ◽

Block Size ◽

Selection Strategy ◽

Secret Data ◽

Unit Distance ◽

Pixel Value ◽

Block Based ◽

Location Map

Recently, Reversible Data Hiding (RDH) techniques has gained much attention in many sensitive fields such as remote sensing, archive management, military and medical image processing systems. This is due to the lossless data extraction ability of RDH schemes. The primary goal of RDH schemes is to achieve high embedding rates while maintaining the quality of cover objects. For achieving better performance, Pixel Value Ordering (PVO) based reversible data hiding schemes have been proposed. PVO refers to the process of ranking the pixels in blocks and then modifying the pixels according to some embedding rules/conditions. So far, the existing PVO techniques have considered neighborhood pixels at unit distance. In this paper, an improved RDH using block based PVO scheme is proposed which exploits the pixel correlation efficiently by increasing the block size and applying a novel Median Pixel based Block Selection Strategy (MPBS). When block size is increased, the ordering of pixels is changed after embedding. So, to extract the secret data in a lossless manner, the secret bits are swapped in accordance with their corresponding Stego pixels’ index order. Also, the overflow and underflow conditions are effectively handled using Location Map. Experimental results show the better performance of the proposed RDH technique with the existing technique.

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A Novel Method for High Capacity Reversible Data Hiding Scheme Using Difference Expansion

International Journal of Natural Computing Research ◽

10.4018/ijncr.2019100102 ◽

2019 ◽

Vol 8 (4) ◽

pp. 13-27

Author(s):

Subhadip Mukherjee ◽

Biswapati Jana

Keyword(s):

Data Hiding ◽

Reversible Data Hiding ◽

Data Extraction ◽

High Capacity ◽

Correlation Coefficients ◽

Extraction Process ◽

Research Area ◽

Difference Expansion ◽

Secret Data ◽

Hiding Capacity

Data hiding techniques are very significant in the research area of information security. In this article, the authors propose a new reversible data hiding (RDH) scheme using difference expansion. At first, the original image is partitioned into 3 × 3 pixel blocks, then marked Type-one and Type-two pixels based on their coordinate values. After that, the authors find correlated pixels by computing correlation coefficients and the median of Type-one pixels. Next, secret data bits are embedded within Type-two pixels based on correlated pixels and Type-one pixels based on the stego Type-two pixels. The data extraction process successfully extracts secret data as well as recovers the cover image. The authors observed the effects of the proposed method by performing experiments on some standard cover images and found significantly better result in terms of data hiding capacity compared with existing data hiding schemes.

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High-Capacity Reversible Data Hiding in Encrypted Images Based on Adaptive Predictor and Compression of Prediction Errors

Mathematics ◽

10.3390/math9172166 ◽

2021 ◽

Vol 9 (17) ◽

pp. 2166

Author(s):

Bin Huang ◽

Chun Wan ◽

Kaimeng Chen

Keyword(s):

Image Encryption ◽

Data Hiding ◽

Reversible Data Hiding ◽

High Capacity ◽

Huffman Coding ◽

Prediction Errors ◽

Original Image ◽

Embedding Capacity ◽

Secret Data ◽

Encrypted Images

Reversible data hiding in encrypted images (RDHEI) is a technology which embeds secret data into encrypted images in a reversible way. In this paper, we proposed a novel high-capacity RDHEI method which is based on the compression of prediction errors. Before image encryption, an adaptive linear regression predictor is trained from the original image. Then, the predictor is used to obtain the prediction errors of the pixels in the original image, and the prediction errors are compressed by Huffman coding. The compressed prediction errors are used to vacate additional room with no loss. After image encryption, the vacated room is reserved for data embedding. The receiver can extract the secret data and recover the image with no errors. Compared with existing approaches, the proposed method efficiently improves the embedding capacity.

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High-Capacity Reversible Data Hiding in Encrypted Images Based on Prediction Error Compression and Block Selection

Security and Communication Networks ◽

10.1155/2021/9606116 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Xu Wang ◽

Li-Yao Li ◽

Ching-Chun Chang ◽

Chih-Cheng Chen

Keyword(s):

Data Hiding ◽

Reversible Data Hiding ◽

Additional Data ◽

High Capacity ◽

Personal Data ◽

Huffman Coding ◽

Prediction Errors ◽

Original Image ◽

Block Based ◽

Encrypted Images

The popularity of cloud computing has impelled more users to upload personal data into the cloud server. The need for secure transmission and privacy protection has become a new challenge and has attracted considerable attention. In this paper, we propose a high-capacity reversible data hiding scheme in encrypted images (RDHEI) that compresses prediction errors in usable blocks of block-based encrypted images. On the content owner side, the original image is divided into 2 × 2 sized blocks, and each block is encrypted by block-based modulation. On the data hider side, an efficient block-based predictor is utilized to generate prediction errors. The Huffman coding technique is introduced to compress prediction errors in the usable blocks to embed abundant additional data. On the receiver side, the additional data can be totally extracted with a data hiding key and the original image can be losslessly recovered with an image encryption key. Experimental results demonstrate that the embedding rate of our proposed scheme is significantly improved compared to those of state-of-the-art schemes.

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Generalized Reversible Data Hiding with Content-Adaptive Operation and Fast Histogram Shifting Optimization

Entropy ◽

10.3390/e23070917 ◽

2021 ◽

Vol 23 (7) ◽

pp. 917

Author(s):

Limengnan Zhou ◽

Hongyu Han ◽

Hanzhou Wu

Keyword(s):

Data Hiding ◽

Reversible Data Hiding ◽

Hot Spot ◽

Data Embedding ◽

Histogram Shifting ◽

Secret Data ◽

Content Selection ◽

Wide Range ◽

Map Generation ◽

Content Adaptive

Reversible data hiding (RDH) has become a hot spot in recent years as it allows both the secret data and the raw host to be perfectly reconstructed, which is quite desirable in sensitive applications requiring no degradation of the host. A lot of RDH algorithms have been designed by a sophisticated empirical way. It is not easy to extend them to a general case, which, to a certain extent, may have limited their wide-range applicability. Therefore, it motivates us to revisit the conventional RDH algorithms and present a general framework of RDH in this paper. The proposed framework divides the system design of RDH at the data hider side into four important parts, i.e., binary-map generation, content prediction, content selection, and data embedding, so that the data hider can easily design and implement, as well as improve, an RDH system. For each part, we introduce content-adaptive techniques that can benefit the subsequent data-embedding procedure. We also analyze the relationships between these four parts and present different perspectives. In addition, we introduce a fast histogram shifting optimization (FastHiSO) algorithm for data embedding to keep the payload-distortion performance sufficient while reducing the computational complexity. Two RDH algorithms are presented to show the efficiency and applicability of the proposed framework. It is expected that the proposed framework can benefit the design of an RDH system, and the introduced techniques can be incorporated into the design of advanced RDH algorithms.

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Taylor expansion based stereo image reversible data hiding

10.21203/rs.3.rs-85033/v1 ◽

2020 ◽

Author(s):

Xinyang Ying ◽

Guobing Zhou

Keyword(s):

Data Hiding ◽

Reversible Data Hiding ◽

Taylor Expansion ◽

Stereo Image ◽

Embedding Capacity ◽

Stereo Images ◽

Secret Data ◽

Stego Image ◽

Multi Level

Abstract The reversible data hiding allows original image to be completely recovered from the stego image when the secret data has been extracted, it is has drawn a lot of attentions from researchers. In this paper, a novel Taylor Expansion (TE) based stereo image reversible data hiding method is presented. Since the prediction accuracy is essential to the data hiding performance, a novel TE based predictor using correlations of two views of the stereo image is proposed. TE can fully exploit strong relationships between matched pixels in the stereo image so that the accuracy of the prediction can be improved. Then, histogram shifting is utilized to embed data to decrease distortion of stereo images, and multi-level hiding can increase embedding capacity. Experimental results show that the proposed method is superior to some existing data hiding methods considering embedding capacity and the quality of the stego stereo images.

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High-Payload Data-Hiding Method for AMBTC Decompressed Images

Entropy ◽

10.3390/e22020145 ◽

2020 ◽

Vol 22 (2) ◽

pp. 145

Author(s):

Jung-Yao Yeh ◽

Chih-Cheng Chen ◽

Po-Liang Liu ◽

Ying-Hsuan Huang

Keyword(s):

Data Hiding ◽

High Capacity ◽

Cover Image ◽

Secret Data ◽

Perceptual Distortion ◽

Hiding Capacity ◽

High Data ◽

Capacity Data ◽

High Payload ◽

Better Than

Data hiding is the art of embedding data into a cover image without any perceptual distortion of the cover image. Moreover, data hiding is a very crucial research topic in information security because it can be used for various applications. In this study, we proposed a high-capacity data-hiding scheme for absolute moment block truncation coding (AMBTC) decompressed images. We statistically analyzed the composition of the secret data string and developed a unique encoding and decoding dictionary search for adjusting pixel values. The dictionary was used in the embedding and extraction stages. The dictionary provides high data-hiding capacity because the secret data was compressed using dictionary-based coding. The experimental results of this study reveal that the proposed scheme is better than the existing schemes, with respect to the data-hiding capacity and visual quality.

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