Reversible Data Hiding Based on Improved Rhombus Predictor and Prediction Error Expansion

Tang, Xin; Zhou, Linna; Liú, Dan; Liu, Boyu; Lu, Xinyi

doi:10.1109/trustcom50675.2020.00016

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…In the past few years, scholars were committed to raising the embedding capacity by improving the prediction accuracy. These algorithms can be classified into two types: 1) Construct great performance prediction methods using local directional information [4,5,6]. 2) Use dynamic prediction methods to calculate pixel values adaptively [7,8,9].…”

Section: Introductionmentioning

confidence: 99%

A high-capacity reversible data hiding based on improved rhombus prediction

Liu¹,

Zhou²,

Zeng³

et al. 2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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Reversible data hiding algorithms based on prediction error histogram of rhombus prediction need excellent prediction performance to achieve more embedding capacity. However, the cost of improving the prediction accuracy is to reduce the number of prediction pixels, which results in a reduction in embedding capacity. It means high prediction accuracy and large embedding capacity become two contradictory conditions. In this paper, we proposed a reversible data hiding algorithm based on improved rhombus prediction, which can break through the upper limit of prediction pixel numbers and increase the embedding capacity. The proposed algorithm utilizes the characteristics of reversible data hiding process to carry out the second stage reversible embedding based on the traditional rhombus prediction. It can embed the additional data into the context pixels of the first stage rhombus prediction so that the embedding capacity will be increased significantly. At the same time, reusing the reversible data hiding characteristics can ensure the lossless recovery of the carrier image and secret information. Experiments show that the algorithm can improve embedding capacity significantly and ensure the high visual performance of marked images.

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Section: Introductionmentioning

confidence: 99%

A high-capacity reversible data hiding based on improved rhombus prediction

Liu¹,

Zhou²,

Zeng³

et al. 2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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“…Reversible data hiding [1][2][3][4] is a novel technique to achieve convert communication, which enables the cover signal to be completely recovered to its original state once the embedded data are extracted. Because of its lossless nature, reversible data hiding has been widely employed in a lot of high fidelity required scenarios, such as medical images and military communications [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…With the help of the proposed framework, those blocks better in smoothness are selected to embed data at first, which largely reduces the invalid shifting and ensures the imperceptibility before full embedding. (2) We propose an improved method to calculate complexity value. Specifically, we assign a distinct weight to each one of the valid context pixels, according to their fluctuation in a small neighborhood, which largely improves the accuracy in complexity evaluation.…”

Section: Introductionmentioning

confidence: 99%

Weighted Average-Based Complexity Calculation in Block Selection Oriented Reversible Data Hiding

Tang

Zhou

Cheng

et al. 2022

Security and Communication Networks

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Complexity of blocks is one of the key factors to influence the performance of reversible data hiding. By embedding data into blocks with smaller complexity, the invalid shifting in pixel value ordering (PVO) based reversible data hiding is largely reduced, thus increasing the imperceptibility. However, the existing techniques are not always accurate in complexity calculation, since context pixels are usually treated equally. As a result, a great number of invalid pixels are assigned too large a weight in complexity calculation, which inevitably affects the precision. In this paper, we propose a weighted average based novel strategy in complexity calculation. In this scheme, we calculate the weight of a certain context pixel with the help of its adjacent neighbors, which ensures the one in a smooth area is assigned a larger weight, and otherwise the opposite. Experimental results show that invalid shifting is largely reduced, thus our scheme is better in imperceptibility compared with the state of the art and has potential application in a lot of fields such as medical imaging.

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