Reversible data hiding method based on pixel expansion and homomorphic encryption

2020 ◽  
Vol 39 (3) ◽  
pp. 2977-2990
Author(s):  
R. Anushiadevi ◽  
Padmapriya Praveenkumar ◽  
John Bosco Balaguru Rayappan ◽  
Rengarajan Amirtharajan
Keyword(s):  
Image Quality ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
Signal To Noise Ratio ◽  
Homomorphic Encryption ◽  
Secret Message ◽  
Pixel Expansion ◽  
Stego Image ◽  
Encrypted Image ◽  
Location Map

Digital image steganography algorithms usually suffer from a lossy restoration of the cover content after extraction of a secret message. When a cover object and confidential information are both utilised, the reversible property of the cover is inevitable. With this objective, several reversible data hiding (RDH) algorithms are available in the literature. Conversely, because both are diametrically related parameters, existing RDH algorithms focus on either a good embedding capacity (EC) or better stego-image quality. In this paper, a pixel expansion reversible data hiding (PE-RDH) method with a high EC and good stego-image quality are proposed. The proposed PE-RDH method was based on three typical RDH schemes, namely difference expansion, histogram shifting, and pixel value ordering. The PE-RDH method has an average EC of 0.75 bpp, with an average peak signal-to-noise ratio (PSNR) of 30.89 dB. It offers 100% recovery of the original image and confidential hidden messages. To protect secret as well as cover the proposed PE-RDH is also implemented on the encrypted image by using homomorphic encryption. The strength of the proposed method on the encrypted image was verified based on a comparison with several existing methods, and the approach achieved better results than these methods in terms of its EC, location map size and imperceptibility of directly decrypted images.

scholarly journals AN EFFECTIVE REVERSIBLE DATA HIDING METHOD BASED ON PIXEL-VALUE-ORDERING

2020 ◽  
Vol 36 (2) ◽  
pp. 139-158
Author(s):  
Nguyen Kim Sao ◽  
Nguyen Ngoc Hoa ◽  
Pham Van At
Keyword(s):  
Image Quality ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
New Method ◽  
Embedding Capacity ◽  
Stego Image ◽  
Pixel Value ◽  
Using Data ◽  
Location Map ◽  
Better Than

This paper presents a new effective reversible data hiding method based on pixel-value-ordering (iGePVO-K) which is improvement of a recent GePVO-K method that recently is considered as a PVO-used method having highest embedding capacity. In comparison with GePVO-K method, iGePVO-K has the following advantages. First, the embedding capacity of the new method is higher than that of GePVO-K method by using data embedding formulas reasonably and reducing the location map size. Second, for embedding data, in the new method, each pixel value is modified at most by one, while in GePVO-K method, each pixel value may be modified by two. In fact, in the GePVO-K method, the largest pixels are modified by two for embedding bits 1 and by one for bits 0. This is also true for the smallest pixels. Meanwhile, in the proposed method, the largest pixels are modified by one for embedding bits 1 and are unchanged if embedding bits 0. Therefore, the stego-image quality in proposed method is better than that in GePVO-K method. Theoretical analysis and experiment results show that the proposed method has higher embedding capacity and better stego image quality than GePVO-K method.

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.

Reversible Data Hiding for Encrypted Image Based on Interpolation Error Expansion

2018 ◽  
Vol 9 (4) ◽  
pp. 76-96 ◽  
Author(s):  
Fuqiang Di ◽  
Minqing Zhang ◽  
Yingnan Zhang ◽  
Jia Liu
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Side Information ◽  
Signal To Noise Ratio ◽  
Prediction Method ◽  
Interpolation Error ◽  
Encrypted Image ◽  
Error Expansion ◽  
Pixel Correlation ◽  
Embedding Performance

A novel reversible data hiding algorithm for encrypted image based on interpolation error expansion is proposed. The proposed method is an improved version of Shiu' s. His work does not make full use of the correlation of the neighbor pixels and some additional side information is needed. The proposed method adopts the interpolation prediction method to fully exploit the pixel correlation and employ the Paillier public key encryption method. The algorithm is reversible. In the proposed method, less side information is demanded. The experiment has verified the feasibility and effectiveness of the proposed method, and the better embedding performance can be obtained, compared with some existing RDHEI-P methods. Specifically, the final embedding capacity can be up to 0.74 bpp (bit per pixel), while the peak signal-to-noise ratio (PSNR) for the marked image Lena is 35 dB. This is significantly higher than Shiu's method which is about 0.5 bpp.

scholarly journals An Improved Reversible Image Transformation Using K-Means Clustering and Block Patching

Information ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 17 ◽  
Author(s):  
Haidong Zhong ◽  
Xianyi Chen ◽  
Qinglong Tian
Keyword(s):  
Image Quality ◽  
Data Hiding ◽  
Privacy Protection ◽  
Reversible Data Hiding ◽  
Auxiliary Information ◽  
Experimental Results ◽  
Image Transformation ◽  
Clustering Method ◽  
Stego Image ◽  
Encrypted Images

Recently, reversible image transformation (RIT) technology has attracted considerable attention because it is able not only to generate stego-images that look similar to target images of the same size, but also to recover the secret image losslessly. Therefore, it is very useful in image privacy protection and reversible data hiding in encrypted images. However, the amount of accessorial information, for recording the transformation parameters, is very large in the traditional RIT method, which results in an abrupt degradation of the stego-image quality. In this paper, an improved RIT method for reducing the auxiliary information is proposed. Firstly, we divide secret and target images into non-overlapping blocks, and classify these blocks into K classes by using the K-means clustering method. Secondly, we match blocks in the last (K-T)-classes using the traditional RIT method for a threshold T, in which the secret and target blocks are paired with the same compound index. Thirdly, the accessorial information (AI) produced by the matching can be represented as a secret segment, and the secret segment can be hided by patching blocks in the first T-classes. Experimental results show that the proposed strategy can reduce the AI and improve the stego-image quality effectively.

A reversible data hiding in encrypted image based on prediction-error estimation and location map

2020 ◽  
Vol 79 (17-18) ◽  
pp. 11591-11614 ◽  
Author(s):  
Asad Malik ◽  
Hong-Xia Wang ◽  
Yanli Chen ◽  
Ahmad Neyaz Khan
Keyword(s):  
Error Estimation ◽  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Encrypted Image ◽  
Location Map

Enhanced stego-image quality and embedding capacity for the partial reversible data hiding scheme

2019 ◽  
Vol 78 (13) ◽  
pp. 18595-18616
Author(s):  
Ching-Nung Yang ◽  
Song-Yu Wu ◽  
Yung-Shun Chou ◽  
Cheonshik Kim
Keyword(s):  
Image Quality ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
Embedding Capacity ◽  
Stego Image

scholarly journals Reversible Data Hiding Algorithm in Fully Homomorphic Encrypted Domain

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 625 ◽  
Author(s):  
Jingxuan Li ◽  
Xingyuan Liang ◽  
Ceyu Dai ◽  
Shijun Xiang
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Additional Data ◽  
Homomorphic Encryption ◽  
Modular Arithmetic ◽  
Original Image ◽  
Encrypted Image ◽  
Group A ◽  
The Difference ◽  
Difference Histogram

This paper proposes a reversible data hiding scheme by exploiting the DGHV fully homomorphic encryption, and analyzes the feasibility of the scheme for data hiding from the perspective of information entropy. In the proposed algorithm, additional data can be embedded directly into a DGHV fully homomorphic encrypted image without any preprocessing. On the sending side, by using two encrypted pixels as a group, a data hider can get the difference of two pixels in a group. Additional data can be embedded into the encrypted image by shifting the histogram of the differences with the fully homomorphic property. On the receiver side, a legal user can extract the additional data by getting the difference histogram, and the original image can be restored by using modular arithmetic. Besides, the additional data can be extracted after decryption while the original image can be restored. Compared with the previous two typical algorithms, the proposed scheme can effectively avoid preprocessing operations before encryption and can successfully embed and extract additional data in the encrypted domain. The extensive testing results on the standard images have certified the effectiveness of the proposed scheme.

Reversible data hiding in an encrypted image using the homomorphic property of elliptic curve cryptography

2021 ◽  
pp. 1-12
Author(s):  
R. Anushiadevi ◽  
Rengarajan Amirtharajan
Keyword(s):  
Elliptic Curve ◽  
Data Hiding ◽  
Elliptic Curve Cryptography ◽  
Reversible Data Hiding ◽  
Cloud Service ◽  
Cover Image ◽  
Stego Image ◽  
Security Applications ◽  
Confidential Data ◽  
Location Map

Reversible Data Hiding (RDH) schemes have recently gained much interest in protecting the secret information and sensitive cover images. For cloud security applications, the third party’s data embedding can be done (e.g., cloud service). In such a scenario, to protect the cover image from unauthorized access, it is essential to encrypt before embedding it. It can be overcome by combining the RDH scheme with encryption. However, the key challenge in integrating RDH with encryption is that the correlation between adjacent pixels begins to disappear after encryption, so reversibility cannot be accomplished. RDH with elliptic curve cryptography is proposed to overcome this challenge. In this paper (ECC-RDH) by adopting additive homomorphism property; the proposed method, the stego image decryption gives the sum of the original image and confidential data. The significant advantages of this method are, the cover image is transferred with high security, the embedding capacity is 0.5 bpp with a smaller location map size of 0.05 bpp. The recovered image and secrets are the same as in the original, and thus 100% reversibility is proved.

Data Hiding Based on Improved Exploiting Modification Direction Method and Huffman Coding

2014 ◽  
Vol 23 (4) ◽  
pp. 451-459 ◽  
Author(s):  
Ali M. Ahmad ◽  
Ghazali Sulong ◽  
Amjad Rehman ◽  
Mohammed Hazim Alkawaz ◽  
Tanzila Saba
Keyword(s):  
Data Hiding ◽  
Signal To Noise Ratio ◽  
Huffman Coding ◽  
Secret Message ◽  
Least Significant Bit ◽  
Stego Image ◽  
Trade Offs ◽  
Secret Code ◽  
Exploiting Modification Direction ◽  
Emd Method

AbstractThe rapid growth of covert activities via communications network brought about an increasing need to provide an efficient method for data hiding to protect secret information from malicious attacks. One of the options is to combine two approaches, namely steganography and compression. However, its performance heavily relies on three major factors, payload, imperceptibility, and robustness, which are always in trade-offs. Thus, this study aims to hide a large amount of secret message inside a grayscale host image without sacrificing its quality and robustness. To realize the goal, a new two-tier data hiding technique is proposed that integrates an improved exploiting modification direction (EMD) method and Huffman coding. First, a secret message of an arbitrary plain text of characters is compressed and transformed into streams of bits; each character is compressed into a maximum of 5 bits per stream. The stream is then divided into two parts of different sizes of 3 and 2 bits. Subsequently, each part is transformed into its decimal value, which serves as a secret code. Second, a cover image is partitioned into groups of 5 pixels based on the original EMD method. Then, an enhancement is introduced by dividing the group into two parts, namely k1 and k2, which consist of 3 and 2 pixels, respectively. Furthermore, several groups are randomly selected for embedding purposes to increase the security. Then, for each selected group, each part is embedded with its corresponding secret code by modifying one grayscale value at most to hide the code in a (2ki + 1)-ary notational system. The process is repeated until a stego-image is eventually produced. Finally, the χ2 test, which is considered one of the most severe attacks, is applied against the stego-image to evaluate the performance of the proposed method in terms of its robustness. The test revealed that the proposed method is more robust than both least significant bit embedding and the original EMD. Additionally, in terms of imperceptibility and capacity, the experimental results have also shown that the proposed method outperformed both the well-known methods, namely original EMD and optimized EMD, with a peak signal-to-noise ratio of 55.92 dB and payload of 52,428 bytes.

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