Pixel-Value-Ordering based Reversible Information Hiding Scheme with Self-Adaptive Threshold Strategy

Lu, Tzu-Chuen; Tseng, Chun-Ya; Shi, Haiyang; Vo, Thanh Nhân

doi:10.3390/sym10120764

Cited by 18 publications

(5 citation statements)

References 21 publications

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“…The information of data embedding is shown in Table 5. The parameter A is the half value of the variable a in Equation (12). It represents the scale of the original value of the predicted image, while B is the integer of the value b × T E , which represents the shifting of the original predicted parameter.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…During the secret data extraction, we compute the difference between the intensity of the pixels, x , and the predicted intensity, x, of the stego image to obtain the modified p e . Based on Equation (12), we find the number of values of the prediction error, p e , and embedded information, b. Finally, we complete the procedures of reversible data hiding.…”

Section: Secret Data Embedding and Extractionmentioning

confidence: 99%

“…The word "reversible" in this context indicates that one can remove the embedded data to restore the original image. In recent years, numerous principles of the reversible information hiding technology were proposed, such as the difference expansion method [3][4][5][6], histogram method [7][8][9][10][11], difference of the pixel value method [12][13][14], the least significant bit method (LSB) [15,16], dual-image-based schemes [17,18], other reversible information hiding schemes [19][20][21][22][23][24][25], and the predictionerror method [26][27][28][29][30][31][32]. A good information hiding theory provides a significant capacity for embedded secret data that cannot be detected [1,33,34].…”

Section: Introductionmentioning

confidence: 99%

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Multilayer Reversible Information Hiding with Prediction-Error Expansion and Dynamic Threshold Analysis

Pan

Huang

Chang

et al. 2022

Sensors

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The rapid development of internet and social media has driven the great requirement for information sharing and intelligent property protection. Therefore, reversible information embedding theory has marked some approaches for information security. Assuming reversibility, the original and embedded data must be completely restored. In this paper, a high-capacity and multilayer reversible information hiding technique for digital images was presented. First, the integer Haar wavelet transform scheme converted the cover image from the spatial into the frequency domain that was used. Furthermore, we applied dynamic threshold analysis, the parameters of the predicted model, the location map, and the multilayer embedding method to improve the quality of the stego image and restore the cover image. In comparison with current algorithms, the proposed algorithm often had better embedding capacity versus image quality performance.

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Section: Experiments and Discussionmentioning

confidence: 99%

Section: Secret Data Embedding and Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multilayer Reversible Information Hiding with Prediction-Error Expansion and Dynamic Threshold Analysis

Pan

Huang

Chang

et al. 2022

Sensors

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“…It resulted in increasing the number of embeddable prediction errors, ultimately leading to enhanced performance. Extending the work of [13,14], a variety of other similar PVO-based methods [15][16][17][18][19][20][21][22][23][24] have come into the picture. The fact of capacity-dependence on the block size is recognised and it is observed that decreasing the block size would increase the capacity.…”

Section: Introductionmentioning

confidence: 99%

High‐quality reversible data hiding scheme using sorting and enhanced pairwise PEE

Kaur

Singh

Rani

et al. 2021

IET Image Processing

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This paper proposes a novel reversible data hiding (RDH) technique using sorting and pairwise prediction error expansion (PEE) to improve embedding capacity (EC) while retaining the quality of cover image. The proposed scheme traverses alternate pixels of the cover image in a zig‐zag order to construct two independent sets for sequential embedding. The pixels of each set are sorted in an increasing order of their rhombus mean followed by a two‐pass data embedding by dividing the sets into 1 × 3 size blocks based on some pre‐defined criteria. In pass 1, two prediction errors are calculated for the first and the last pixels using their rhombus means; and pairwise mapping is modified and exploited to embed the secret data in such a way that the value of the first pixel is either increased or remains unchanged, and the value of the last pixel is either decreased or remains unchanged. In pass‐2, the middle pixel is utilised to predict the first and last pixels and the values of the first pixel and the last pixel are either decreased or remains unchanged and either increased or remains unchanged, respectively. In contrast to some existing recovery‐based methods, the proposed pass‐2 guarantees to complement the changes made in pass‐1, thereby boosting the quality along with increased EC. The experimental results show that the proposed method achieves better embedding performance than the state‐of‐the‐art RDH techniques. More specifically, the proposed method gets an increment by an average of 0.46 dB and 1.01 dB for embedding 10,000 bits and 20,000 bits respectively over its closest prior art.

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“…The PVO-based information hiding method has been increasingly investing in research in recent years. Lu et al (2018) [27] design an adaptive threshold generation mechanism for different images to achieve high image quality. Lee et al [28] proposed the ''star-shaped'' PVO method, which fully exploits the similarity of adjacent pixels in each 3×3 block to maximize the embedding of secret data.…”

Section: Introductionmentioning

confidence: 99%

Reversible Data Hiding Scheme Based on Quad-Tree and Pixel Value Ordering

Lee

Chang

et al. 2019

IEEE Access

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This study proposes a reversible data hiding (RDH) scheme based on pixel value ordering (PVO). RDH schemes based on PVO technique generally realize the prediction procedure by employing the sorted pixels in a block-wise manner, and then embedding secret data by expanding the prediction errors. Since these operations are carried out based on the block, and pixel intensity has spatial correlation such that the neighboring pixels have similar values, the block patterns and sizes certainly affect the embedding performance of the PVO-based RDH method. The original PVO-based RDH schemes use fixed block size. However, due to the differences in image complexity, fixed-size blocks have many limitations. Thus far, several methods that break the limitations have been proposed, such as setting a threshold value, and determine the size from the complexity of the block, using sliding window instead of fixed blocks. These methods significantly enhanced the embedding performance of PVO-based RDH schemes, but this can still be improved. The proposed scheme introduces the quad-tree structure, which combines the advantages of several previously proposed methods to design a flexible block patterns, thus fully utilizing the characteristics of the image itself in embedding secret data. Experimental results indicate that the proposed scheme is flexible and better than previous approaches. INDEX TERMS Pixel value ordering, prediction error expansion, reversible data hiding, quad-tree.

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Pixel-Value-Ordering based Reversible Information Hiding Scheme with Self-Adaptive Threshold Strategy

Cited by 18 publications

References 21 publications

Multilayer Reversible Information Hiding with Prediction-Error Expansion and Dynamic Threshold Analysis

Multilayer Reversible Information Hiding with Prediction-Error Expansion and Dynamic Threshold Analysis

High‐quality reversible data hiding scheme using sorting and enhanced pairwise PEE

Reversible Data Hiding Scheme Based on Quad-Tree and Pixel Value Ordering

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