An adaptive reversible data hiding scheme based on prediction error histogram shifting by exploiting signed-digit representation

Xie, Xiaozhu; Chang, Chin‐Chen; Hu, Yu‐Chen

doi:10.1007/s11042-019-08402-6

Cited by 19 publications

(25 citation statements)

References 24 publications

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“…In order to compare the presented algorithm with other lossless steganography methods in the same environment, embeddable blocks, which could be used for hiding information without causing inter-view and intra-frame distortion drift, are selected from P0 frames by using three conditions and coupling coefficients. HS [8], DE [15], and IPS [16] are three typical lossless steganography algorithms and could be used for video. Therefore, they are chosen for comparing with our algorithm.…”

Section: Comparison Of Different Lossless Steganography Methodsmentioning

confidence: 99%

“…Coupling coefficients denoted as (q1,y1), (q2,y2),…, (qS, yS)(S∈ [2,8]) are divided into embedding coefficients and compensation coefficients. The former can compose an embedding carrier vector denoted as ψc = (q1, q2, …, qi, …, qS).…”

Section: Information Embeddingmentioning

confidence: 99%

“…Histogram shifting (HS) [3][4][5][6][7][8][9][10] and difference expansion (DE) [11][12][13][14][15] are two principal lossless steganography approaches. In addition, integer pair swap (IPS) [16], pair-wise logical computation [17], and some other methods [18] [19] have been employed to hide information reversibly.…”

Section: Introductionmentioning

confidence: 99%

“…In [6], the closest adjacent pixels are used to predict the visited pixel value and evaluate its just noticeable difference. Prediction-error shift is used to improve the embedding performance in [8,9]. In [9], multiple pairs of expansion bins are utilized for each histogram , and the multiple-expansion-bin-selection for optimal embedding is formulated as an optimization problem.…”

Section: Introductionmentioning

confidence: 99%

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Lossless Steganography on Orthogonal Vector for 3D H.264 with Limited Distortion Diffusion

Zhao¹,

Li²

2021

Computer Science &Amp; Information Technology (CS &Amp; IT)

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In order to improve the undetectability, a lossless algorithm based on orthogonal vectors with limited distortion diffusion for 3D H.264 video is proposed in this paper. Inter-view distortion drift is avoided by embedding data into frames, which do not predict other views. Three conditions and pairs of coefficients are proposed to prevent intra-frame distortion diffusion. Several quantized discrete cosine transform coefficients are chosen from an embeddable luminance 4×4 block to construct a carrier vector, which is modified by an offset vector. When the carrier vector and the offset vector are orthogonal or near to be orthogonal, a data bit can be hidden. Experimental results indicate that the method is effective by enhancing peak signal-to-noise ratio with 7.5dB and reducing the Kullback-Leibler divergence with 0.07 at least. More than 1.7×1015 ways could be utilized for constructing the vectors, so it is more difficult for others to steal data.

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Section: Comparison Of Different Lossless Steganography Methodsmentioning

confidence: 99%

Section: Information Embeddingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Lossless Steganography on Orthogonal Vector for 3D H.264 with Limited Distortion Diffusion

Zhao¹,

Li²

2021

Computer Science &Amp; Information Technology (CS &Amp; IT)

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“…The [25] proposed method has a real-time performance. Besides, several data hiding algorithms based on prediction errors including [12][13][14][15][16][17]24] and an algorithm based on prediction error hiding for color images [18] are proposed. There are [20][21][22][23][26][27][28][29][30][31][32][33][34][35][36] in the reversible data hiding field worth mentioning recently.…”

Section: Introductionmentioning

confidence: 99%

A Reversible Data Hiding Algorithm Based on Prediction Error With Large Amounts of Data Hiding in Spatial Domain

Sun

et al. 2020

IEEE Access

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In recent years among data hiding technologies, Reversible Data Hiding(RDH) technology has attracted widespread interest and application, which is to hide the secret information in a carrier image and recover the original carrier image losslessly to extract the secret information. Current research on RDH algorithms mainly involving frequency domain, spatial domain, and encryption domain. Based on the Prediction-Error Expansion(PEE) methods, as spatial domain approaches, achieved great progress in the past decade. However, there is a defect in the state-of-the-art methods that with the embedded payload increased, the distortion rate of the cover image increased simultaneously. To solve the problem, we proposed a refined reversible data hiding algorithm based on the PEE method with simple implementation. We improved an effective predictor that all the remaining pixels can be predicted in the embedding process, except for those in the first row, the first column, the last row, and the last column in the original image. The extraction process is the reverse of the embedding process that the embedded information and the original carrier is restored without damage. Our work utilized the correlation between image pixels better to solve the inherent contradiction between payload and distortion rate in the state-of-the-art data hiding algorithms. Proven through experiments, our method achieved a large embedding capacity while keeping the image distortion rate and computing complexity low. INDEX TERMS reversible data hiding (RDH), prediction-error expansion (PEE), watermarking.

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Reversible Data Hiding in Encrypted Images Based on Block Classification Coding of Sparse Representation

Wu,

Wang,

Zhang

et al. 2023

Lecture Notes in Computer Science

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An adaptive reversible data hiding scheme based on prediction error histogram shifting by exploiting signed-digit representation

Cited by 19 publications

References 24 publications

Lossless Steganography on Orthogonal Vector for 3D H.264 with Limited Distortion Diffusion

Lossless Steganography on Orthogonal Vector for 3D H.264 with Limited Distortion Diffusion

A Reversible Data Hiding Algorithm Based on Prediction Error With Large Amounts of Data Hiding in Spatial Domain

Reversible Data Hiding in Encrypted Images Based on Block Classification Coding of Sparse Representation

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