General Framework to Histogram-Shifting-Based Reversible Data Hiding

Li, Xiaolong; Li, Bin; Yang, Bin; Zeng, Tieyong

doi:10.1109/tip.2013.2246179

Cited by 389 publications

(122 citation statements)

References 35 publications

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“…The Reversible watermarking of [5] is very efficient. Here investigates the use of local LS prediction in DE reversible watermarking.…”

Section: IImentioning

confidence: 99%

Enhancing Security of Image by Steganography using Reversible Texture Synthesis

Vinod¹,

Sony²

2017

IJERT

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Abstract-Steganography is a method of information hiding. In this paper, introduce a novel approach for enhancing the security of an image by steganography using reversible texture synthesis. The texture synthesis process produces an arbitrary large and similar looking new texture from a small texture image. This algorithm conceals the source texture and embeds the secret image through the process of texture synthesis. So we can extract the secret image and the source texture from a stego synthetic texture. This method offers four distinct advantages. First, the embedding capacity is proportional to the size of the stego synthetic texture image. Second, it is very difficult for a steganalytic algorithm to defeat this steganography method. Third, recovery of the source texture from the stego image is done. Fourth, prevent loss of information due to the mirroring operation. The proposed algorithm is secure and robust against an RS steganalysis attack.

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“…The Reversible watermarking of [5] is very efficient. Here investigates the use of local LS prediction in DE reversible watermarking.…”

Section: IImentioning

confidence: 99%

Enhancing Security of Image by Steganography using Reversible Texture Synthesis

Vinod¹,

Sony²

2017

IJERT

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“…However, the average embedding capacity of their scheme was quite a bit lower than 0.1 bpp for a single embedding level, because the reference pixels or reference sub-images were not used for embedding data. Many reversible, histogram-based data hiding schemes [9]- [12] have been proposed to further improve the quality of the stego images and the embedding capacity provided by Ni et al's scheme.…”

Section: Introductionmentioning

confidence: 99%

Reversible Data Hiding Using Pixel Order Exchange

Wu¹,

Chang²,

Nguyen³

2017

IJFCC

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Abstract-Reversible data hiding is a technique that extracts secret data exactly and recovers the cover image completely. Many reversible data hiding schemes have been proposed in the literature for various goals. However, many drawbacks are discovered in these schemes, which are low visual quality, limited embedding capacity and a large size of the location map. In this paper, we propose a new reversible data hiding scheme using pixel order exchange to enhance the embedding capacity and the visual quality of the stego image. In addition, the size of the location map in our proposed scheme is very small. In the proposed scheme, the cover image is processed first. Then, the order of pixels in a pair is exchanged to embed secret data, while guaranteeing only a small distortion of the stego image. Our experimental results indicated that the proposed scheme provides larger embedding capacity and better image quality than some previous schemes.Index Terms-Reversible data hiding, pixel order exchange, good image quality, high embedding capacity.

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“…They achieved this by using prediction error (PE) instead of the difference of two connected pixels, which produces less error and decreases image degradation significantly. Their technique is to expand the PE whose predicted value is calculated from the predictor; therefore, this technique can be improved by utilizing a high performance predictor such as the median edge detector predictor (MED) [5]- [8], the gradient-adjusted predictor (GAP) [23], the rhombus predictor [9], [10], [19] and improved rhombus predictor [22], partial differential equation (PDE) predictor [11], Gaussian weight predictor [12], [13], or non-uniform weight predictor [24]. Several researchers had tried to increase the prediction function efficiency as in Shaowei et al work's [17].…”

Section: Introductionmentioning

confidence: 99%

Optimal Gaussian Weight Predictor and Sorting Using Genetic Algorithm for Reversible Watermarking Based on PEE and HS

Panyindee

Pintavirooj

2016

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper introduces a reversible watermarking algorithm that exploits an adaptable predictor and sorting parameter customized for each image and each payload. Our proposed method relies on a wellknown prediction-error expansion (PEE) technique. Using small PE values and a harmonious PE sorting parameter greatly decreases image distortion. In order to exploit adaptable tools, Gaussian weight predictor and expanded variance mean (EVM) are used as parameters in this work. A genetic algorithm is also introduced to optimize all parameters and produce the best results possible. Our results show an improvement in image quality when compared with previous conventional works. key words: prediction-error expansion (PEE), histogram shifting (HS), reversible watermarking, Gaussian weight predictor, expanded variance mean (EVM)

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General Framework to Histogram-Shifting-Based Reversible Data Hiding

Cited by 389 publications

References 35 publications

Enhancing Security of Image by Steganography using Reversible Texture Synthesis

Enhancing Security of Image by Steganography using Reversible Texture Synthesis

Reversible Data Hiding Using Pixel Order Exchange

Optimal Gaussian Weight Predictor and Sorting Using Genetic Algorithm for Reversible Watermarking Based on PEE and HS

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