A novel reversible data hiding method with image contrast enhancement

Wu, Hao; Tang, Shaohua; Huang, Jiwu; Shi, Yun Q.

doi:10.1016/j.image.2017.12.006

Cited by 67 publications

(91 citation statements)

References 29 publications

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“…Chen et al [34] constructed an asymmetric difference histogram mechanism to reduce the embedding distortion. Wu et al [35] merged the adjacent histogram bins and embedded the watermark using the order-retaining modification method. Ou et al [36] sorted the pixels within sub-blocks to maintain the size and order of the pixel when embedding the watermark using the multilayer histogram shifting method.…”

Section: Reversible Watermarking Based On Histogram Shiftingmentioning

confidence: 99%

A Reversible Watermarking Scheme for Vector Maps Based on Multilevel Histogram Modification

2018

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To protect the security of vector maps, we propose a novel reversible watermarking scheme for vector maps based on a multilevel histogram modification. First, a difference histogram is constructed using the correlations of adjacent coordinates, and the histogram is divided into continuous regions and discontinuous regions by combining the characteristics of vector map data. Second, the histogram bins that require modification are determined in the continuous regions through the optimal peak value, and the peak values are chosen from the flanking discontinuous regions in both directions; the watermarks are embedded by adopting the multilevel histogram modification strategy. The watermark extraction process is the reverse of the embedding process, and after completing the watermark extraction, the carrier data can be recovered losslessly. The experimental results show that the proposed algorithm has good invisibility and is completely reversible. Compared with similar algorithms reported previously, it achieves higher watermark embedding capacity under the same embedding distortion with lower complexity, thereby having a higher application value.

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Section: Reversible Watermarking Based On Histogram Shiftingmentioning

confidence: 99%

A Reversible Watermarking Scheme for Vector Maps Based on Multilevel Histogram Modification

2018

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“…This important technique is widely used in medical images, where no distortion of the original image is allowed. Different from natural images, medical images are often demanded that the contrast of images can be enhanced for helping doctors to diagnose [Wu, Dugelay and Shi (2015); Wu, Tang, Huang et al (2018); Gao and Shi (2015); Yang, Zhang, Liang et al (2016); Gao, Wan, Yao et al (2017); Yang, Zhang, Liang et al (2018)]. And data to be embedded is not limited to authentication information, it may be information about the patient's privacy, such as personal information, diagnostic records and results.…”

Section: Introductionmentioning

confidence: 99%

“…There are the state-of-the-art RDH methods with contrast enhancement as follows. Wu et al [Wu, Dugelay and Shi (2015)] expanded the peakpairs of gray histograms to embed data to enhance image's contrast, and they improved image preprocessing to achieve the higher payload later [Wu, Tang, Huang et al (2018)]. On the basis of HS, Gao et al [Gao and Shi (2015)] added wavelet domain to embed more data.…”

Section: Introductionmentioning

confidence: 99%

“…Yang et al [Yang, Zhang, Liang et al (2018)] adopted HS to embed data respectively into region of interest and region of noninterest (NROI) which were segmented by ATD. Through analyzing the existing RDH methods with contrast enhancement, we find that the contrast enhancement of these methods [Wu, Dugelay and Shi (2015); Wu, Tang, Huang et al (2018); Gao and Shi (2015); Yang, Zhang, Liang et al (2016)] are not obvious at the low embedding rate and the segmentation ways [Gao, Wan, Yao et al (2017); Yang, Zhang, Liang et al (2018)] which are based on threshold are not intuitive for doctors to diagnose. Therefore, the more goal-oriented segmentation scheme and RDH method with obvious contrast enhancement are required for medical images.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Reversible Data Hiding Scheme Based on Lesion Extraction and with Contrast Enhancement for Medical Images

Xiao¹,

Yang²,

Li³

et al. 2019

Computers, Materials &Amp; Continua

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The medical industry develops rapidly as science and technology advance. People benefit from medical resource sharing, but suffer from privacy leaks at the same time. In order to protect patients' privacy and improve quality of medical images, a novel reversible data hiding (RDH) scheme based on lesion extraction and with contrast enhancement is proposed. Furthermore, the proposed scheme can enhance the contrast of medial image's lesion area directly and embed high-capacity privacy data reversibly. Different from previous segmentation methods, this scheme first adopts distance regularized level set evolution (DRLSE) to extract lesion and targets at the lesion area accurately for medical images. Secondly, the data is embedded into the lesion area by improved histogram shifting method to enhance the contrast of medial image's lesion area. Lastly, the rest of data is embedded into the non-lesion area by the high-capacity embedding method to achieve the higher payload. At the receiving end, data can be extracted completely and images can be recovered losslessly by the third party with right. Experimental results have shown that the method of lesion extraction has an advantage over the existing segmentation methods in medical images. The image quality is improved well and the performance of contrast enhancement in the lesion area is better than other RDH methods with contrast enhancement.

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“…DE based algorithms usually shift the difference of neighboring pixels for creating a vacant least significant bit (LSB) and append one secret bit to the vacated LSB [5,6]. The HS based algorithms firstly shift the bins of histogram of gray values [7] or the predicted errors [8][9][10][11][12] for generating vacated space and then embed secret data into the vacated space. This kind of algorithms can provide a good trade-off between embedding capacity and visual quality.…”

Section: Introductionmentioning

confidence: 99%

Reversible Data Hiding with Pixel Prediction and Additive Homomorphism for Encrypted Image

Zhang

Tang

et al. 2018

Security and Communication Networks

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Data hiding in encrypted image is a recent popular topic of data security. In this paper, we propose a reversible data hiding algorithm with pixel prediction and additive homomorphism for encrypted image. Specifically, the proposed algorithm applies pixel prediction to the input image for generating a cover image for data embedding, referred to as the preprocessed image. The preprocessed image is then encrypted by additive homomorphism. Secret data is finally embedded into the encrypted image via modular 256 addition. During secret data extraction and image recovery, addition homomorphism and pixel prediction are jointly used. Experimental results demonstrate that the proposed algorithm can accurately recover original image and reach high embedding capacity and good visual quality. Comparisons show that the proposed algorithm outperforms some recent algorithms in embedding capacity and visual quality.

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A novel reversible data hiding method with image contrast enhancement

Cited by 67 publications

References 29 publications

A Reversible Watermarking Scheme for Vector Maps Based on Multilevel Histogram Modification

A Reversible Watermarking Scheme for Vector Maps Based on Multilevel Histogram Modification

A Novel Reversible Data Hiding Scheme Based on Lesion Extraction and with Contrast Enhancement for Medical Images

Reversible Data Hiding with Pixel Prediction and Additive Homomorphism for Encrypted Image

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