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Proceedings ArticleDOI

Efficient adaptive prediction based reversible image watermarking

01 Sep 2013-Vol. 20, Iss: 12, pp 4540-4544
TL;DR: A new reversible watermarking algorithm based on additive prediction-error expansion which can recover original image after extracting the hidden data is proposed and has a better embedding capacity and also gives better Peak Signal to Noise Ratio (PSNR) as compared to state-of-the-art reversible watermarked schemes.

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Abstract: In this paper, we propose a new reversible watermarking algorithm based on additive prediction-error expansion which can recover original image after extracting the hidden data. Embedding capacity of such algorithms depend on the prediction accuracy of the predictor. We observed that the performance of a predictor based on full context prediction is preciser as compared to that of partial context prediction. In view of this observation, we propose an efficient adaptive prediction (EAP) method based on full context, that exploits local characteristics of neighboring pixels much effectively than other prediction methods reported in literature. Experimental results demonstrate that the proposed algorithm has a better embedding capacity and also gives better Peak Signal to Noise Ratio (PSNR) as compared to state-of-the-art reversible watermarking schemes.

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Citations
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Journal ArticleDOI
TL;DR: A completely reversible data hiding method for ECG (Electrocardiogram) data that can find out false ownership claims as well as detect the tampered region of ECG data and 100% reversibility is proposed.

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Abstract: Biomedical signals serves as a backbone of telemedicine industry and its analysis help the remote diagnosis possible. Security of biomedical data is one of the critical issues during its transmission from one storage device to another storage device. In this work, a completely reversible data hiding method is proposed for the ECG (Electrocardiogram) data. The proposed scheme is designed in such a way that it can find out false ownership claims as well as detect the tampered region of ECG data. In addition, original ECG signal is perfectly reconstructed from watermarked signal. The reversibility is tested for a large set of 460 different ECG signals generated from MIT-BIH arrhythmia database and complete reversibility (100%) is obtained each time. In proposed approach, deep neural network is used for an improved error prediction and prediction error expansion (PEE) is combined with it to ensure reversibility. Proposed method is a high capacity method and data hiding of 0.99 bps (bits per sample) is achieved with very little distortion in watermarked signal. The performance of proposed work is evaluated through percentage residual difference (PRD), signal to noise ratio (SNR) and normalized cross correlation (NCC). The most important contribution of proposed work is its multipurpose nature: ownership detection, tamper localization and 100% reversibility. Tamper detection and localization performance of the proposed method is also tested against different attacks and it is found to be quite good.

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20 citations


Journal ArticleDOI
TL;DR: An extensive survey of the state-of-the-art in reversible watermarking techniques for relational databases is made to reflect recent research progress and to point out the key issues for future research.

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Abstract: Over the past few years, reversible watermarking techniques for relational databases have been proposed to provide protection of ownership rights, data tempering, and data integrity. Mainly, these techniques ensure original data recovery from watermarked data, whereas irreversible watermarking schemes only protect ownership rights. This characteristic of reversible watermarking has emerged as a candidate solution for the protection of ownership rights of data, intolerable to modifications such as medical data, genetic data, credit card, and bank account data. The main objective of this paper is to make an extensive survey of the state-of-the-art in reversible watermarking techniques for relational databases to reflect recent research progress and to point out the key issues for future research. In order to analyze these techniques, a classification has been performed on the basis of i the extent of modifications introduced by the watermarking scheme in the underlying data and ii the robustness of the embedded watermark against malicious attacks. Copyright © 2015 John Wiley & Sons, Ltd.

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15 citations


Cites methods from "Efficient adaptive prediction based..."

  • ...In [49], an additive prediction-error expansion reversible watermarking technique for images is proposed....

    [...]

  • ...Prediction error expansion watermarking (PEEW) techniques [11,26,43–48], and [49] incorporate a predictor in place of a difference operator to select candidate pixels or features for embedding watermark information....

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Journal ArticleDOI
Aniket Roy1, Rajat Subhra Chakraborty1Institutions (1)
TL;DR: This paper proves that the bounded capacity distortion minimization problem for prediction error expansion-based reversible watermarking schemes is NP-hard, and the corresponding decision version of the problem isNP-complete, and finds the optimal performance metric values for a given image using concepts from the optimal linear prediction theory.

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Abstract: Reversible image watermarking is a technique that allows the cover image to remain unmodified after watermark extraction. Prediction error expansion-based schemes are currently the most efficient and widely used class of reversible image watermarking techniques. In this paper, first, we prove that the bounded capacity distortion minimization problem for prediction error expansion-based reversible watermarking schemes is NP-hard, and the corresponding decision version of the problem is NP-complete. Then, we prove that the dual problem of bounded distortion capacity maximization problem for prediction error expansion-based reversible watermarking schemes is NP-hard, and the corresponding decision problem is NP-complete. Furthermore, taking advantage of the integer linear programming formulations of the optimization problems, we find the optimal performance metric values for a given image, using concepts from the optimal linear prediction theory. Our technique allows the calculation of these performance metric limit without assuming any particular prediction scheme. The experimental results for several common benchmark images are consistent with the calculated performance limits validate our approach.

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7 citations


Book ChapterDOI
Jiaxin Yin1, Rui Wang1, Yuanfang Guo1, Feng Liu1Institutions (1)
17 Sep 2016-
TL;DR: Experimental results demonstrate that the proposed adaptive reversible data hiding method can achieve a higher image quality and a less increased file size compared to the current state-of-the-art RDH method for JPEG images.

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Abstract: Currently JPEG is the most popular image file format and the majority of images are stored in JPEG format due to storage constraint. Recently, reversible data hiding (RDH) for JPEG images draws researchers attention and has been developed rapidly. Due to the compression, performing RDH on a typical JPEG image is much more difficult than that on an uncompressed image. In this paper, we propose an adaptive reversible data hiding method for JPEG images, which is based on histogram shifting. We propose to select the optimal expandable bins-pair at image level by adopting a k-th nearest neighbors (KNN) algorithm. By developing a new block selection strategy, we can adaptively select the to-be-embedded blocks. Then, the message bits are embedded into the selected blocks at a specific bins-pair via the histogram shifting algorithm. Experimental results demonstrate that our proposed method can achieve a higher image quality and a less increased file size compared to the current state-of-the-art RDH method for JPEG images.

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5 citations


Cites methods from "Efficient adaptive prediction based..."

  • ...Ordinary RDH algorithms, such as histogram shifting [9], difference expansion [6,14], pixel groups’ geometric structure [18], DNA XNOR [15] etc....

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Book ChapterDOI
01 Oct 2014-
TL;DR: An additive prediction error expansion (PEE) based reversible data hiding scheme that gives overall low distortion and relatively high embedding capacity and outperforms the state-of-the-art algorithms both in terms of embeddingcapacity and Peak Signal to Noise Ratio.

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Abstract: In this paper, we present an additive prediction error expansion (PEE) based reversible data hiding scheme that gives overall low distortion and relatively high embedding capacity. Recently reported interpolation based PEE method uses fixed order predictor that fails to exploit the correlation between the neighborhood pixels and the unknown pixel (to be interpolated). We observed that embedding capacity and distortion of PEE based algorithm depends on the prediction accuracy of the predictor. In view of this observation, we propose an interpolation based method that predicts pixels using predictors of different structure and order. Moreover, we use only original pixels for interpolation. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms both in terms of embedding capacity and Peak Signal to Noise Ratio.

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5 citations


Cites methods from "Efficient adaptive prediction based..."

  • ...SJ [10] 66,512 22,685 42,241 91,890 IPEE [11] 71,674 22,696 38,734 84,050 Proposed 74,412 25,341 45,651 95,890...

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  • ...The relationship between e(n) and ew(n) is discussed in method [10] and is given as follows: When an original pixel undergoes watermarking processes by (2), the distortion caused be the process is I(n) − Iw(n) = (P (n) + e(n)) − (P (n) + ew(n)) = e(n) − ew(n) ≤ Q....

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References
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Journal ArticleDOI
TL;DR: The redundancy in digital images is explored to achieve very high embedding capacity, and keep the distortion low, in a novel reversible data-embedding method for digital images.

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Abstract: Reversible data embedding has drawn lots of interest recently Being reversible, the original digital content can be completely restored We present a novel reversible data-embedding method for digital images We explore the redundancy in digital images to achieve very high embedding capacity, and keep the distortion low

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2,444 citations


"Efficient adaptive prediction based..." refers methods in this paper

  • ...categories [2]: data compression [3] based RIW, histogram based shifting methods [4]-[6] and difference expansion (DE) [7]-[10] based RIW....

    [...]


Journal ArticleDOI
Zhicheng Ni1, Yun-Qing Shi1, Nirwan Ansari1, Wei Su2Institutions (2)
TL;DR: It is proved analytically and shown experimentally that the peak signal-to-noise ratio of the marked image generated by this method versus the original image is guaranteed to be above 48 dB, which is much higher than that of all reversible data hiding techniques reported in the literature.

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Abstract: A novel reversible data hiding algorithm, which can recover the original image without any distortion from the marked image after the hidden data have been extracted, is presented in this paper. This algorithm utilizes the zero or the minimum points of the histogram of an image and slightly modifies the pixel grayscale values to embed data into the image. It can embed more data than many of the existing reversible data hiding algorithms. It is proved analytically and shown experimentally that the peak signal-to-noise ratio (PSNR) of the marked image generated by this method versus the original image is guaranteed to be above 48 dB. This lower bound of PSNR is much higher than that of all reversible data hiding techniques reported in the literature. The computational complexity of our proposed technique is low and the execution time is short. The algorithm has been successfully applied to a wide range of images, including commonly used images, medical images, texture images, aerial images and all of the 1096 images in CorelDraw database. Experimental results and performance comparison with other reversible data hiding schemes are presented to demonstrate the validity of the proposed algorithm.

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2,004 citations


Journal ArticleDOI
D.M. Thodi1, Jeffrey J. Rodriguez1Institutions (1)
TL;DR: The experimental results for many standard test images show that prediction-error expansion doubles the maximum embedding capacity when compared to difference expansion, and there is a significant improvement in the quality of the watermarked image, especially at moderate embedding capacities.

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Abstract: Reversible watermarking enables the embedding of useful information in a host signal without any loss of host information. Tian's difference-expansion technique is a high-capacity, reversible method for data embedding. However, the method suffers from undesirable distortion at low embedding capacities and lack of capacity control due to the need for embedding a location map. We propose a histogram shifting technique as an alternative to embedding the location map. The proposed technique improves the distortion performance at low embedding capacities and mitigates the capacity control problem. We also propose a reversible data-embedding technique called prediction-error expansion. This new technique better exploits the correlation inherent in the neighborhood of a pixel than the difference-expansion scheme. Prediction-error expansion and histogram shifting combine to form an effective method for data embedding. The experimental results for many standard test images show that prediction-error expansion doubles the maximum embedding capacity when compared to difference expansion. There is also a significant improvement in the quality of the watermarked image, especially at moderate embedding capacities

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1,069 citations


Journal ArticleDOI
Abstract: We present a novel lossless (reversible) data-embedding technique, which enables the exact recovery of the original host signal upon extraction of the embedded information. A generalization of the well-known least significant bit (LSB) modification is proposed as the data-embedding method, which introduces additional operating points on the capacity-distortion curve. Lossless recovery of the original is achieved by compressing portions of the signal that are susceptible to embedding distortion and transmitting these compressed descriptions as a part of the embedded payload. A prediction-based conditional entropy coder which utilizes unaltered portions of the host signal as side-information improves the compression efficiency and, thus, the lossless data-embedding capacity.

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940 citations


Book ChapterDOI
Zhicheng Ni1, Yun-Qing Shi1, Nirwan Ansari1, Wei Su1Institutions (1)
25 May 2003-
TL;DR: A theoretical proof and numerous experiments show that the PSNR of the marked image generated by this method is always above 48 dB, which is much higher than other reversible data hiding algorithms.

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Abstract: This paper presents a novel reversible data hiding algorithm, which can recover the original image without distortion from the marked image after the hidden data have been extracted. This algorithm utilizes the zero or the minimum point of the histogram and slightly modifies the pixel values to embed data. It can embed more data as compared to most of the existing reversible data hiding algorithms. A theoretical proof and numerous experiments show that the PSNR of the marked image generated by this method is always above 48 dB, which is much higher than other reversible data hiding algorithms. The algorithm has been applied to a wide range of different images successfully. Some experimental results are presented to demonstrate the validity of the algorithm.

...read more

666 citations


"Efficient adaptive prediction based..." refers methods in this paper

  • ...categories [2]: data compression [3] based RIW, histogram based shifting methods [4]-[6] and difference expansion (DE) [7]-[10] based RIW....

    [...]

  • ...Ni [4] 5,460 5,421 7,301 16,171 Lin [5] 59,900 19,130 37,644 80,006 Hu [10] 60,241 21,411 28,259 77,254 EAP 66,512 22,685 42,241 91,890...

    [...]


Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
20203
20195
20183
20173
20162
20151