Reversible Watermarking Based on Invariant Image Classification and Dynamic Histogram Shifting

Coatrieux, Gouenou; Pan, Wei; Cuppens-Boulahia, Nora; Cuppens, Frédéric; Roux, Christian

doi:10.1109/tifs.2012.2224108

Cited by 199 publications

(65 citation statements)

References 17 publications

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“…The current paper is an extended version of [7] and includes a new watermarking scheme with smaller distortion compared to [7], [14] and better steganalytic properties that make it practically undetectable by the developed steganalytic attack. The embedding algorithm is based on histogram shape modification, similarly to some existing image watermarking algorithms [31], [32].…”

Section: Related Workmentioning

confidence: 99%

A 3D Steganalytic Algorithm and Steganalysis-Resistant Watermarking

Yang

Pintus

Rushmeier

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. We also propose a modification of Cho et al.'s watermarking algorithms with the watermark embedded by changing the histogram of the radial coordinates of the vertices. Rather than targeting a continuous statistics such as the mean or variance of the values in a bin, the proposed watermarking modifies a discrete statistic, which here is the height of the histogram bin, to achieve watermark embedding. Experimental results demonstrate that the modified algorithm offers not only better resistance against the steganalytic attack we developed, but also an improved robustness/capacity trade-off.

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Section: Related Workmentioning

confidence: 99%

A 3D Steganalytic Algorithm and Steganalysis-Resistant Watermarking

Yang

Pintus

Rushmeier

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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“…In addition to facilitating original DNA recovery, reversible DNA data hiding can prevent DNA forgery and mutations from external data, and the mutation process can be analyzed using the iterative process of embedding, detecting, and recovering the information. Reversible image data hiding (or watermarking) has been investigated in many studies using difference expansion [21], prediction error expansion [22][23][24], histogram shifting [25,26], lossless compression 2 Security and Communication Networks [27], and quantization index modulation [28], among other methods. In addition, the performance analysis of relevant methods has been reported [29], and it has been shown that expansion-based methods are more effective than other methods in terms of capacity, imperceptibility, and computation.…”

Section: Introductionmentioning

confidence: 99%

Reversible Data Hiding for DNA Sequence Using Multilevel Histogram Shifting

Lee

2018

Security and Communication Networks

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A large number of studies have examined DNA storage to achieve information hiding in DNA sequences with DNA computing technology. However, most data hiding methods are irreversible in that the original DNA sequence cannot be recovered from the watermarked DNA sequence. This study presents reversible data hiding methods based on multilevel histogram shifting to prevent biological mutations, preserve sequence length, increase watermark capacity, and facilitate blind detection/recovery. The main features of our method are as follows. First, we encode a sequence of nucleotide bases with four-character symbols into integer values using the numeric order. Second, we embed multiple bits in each integer value by multilevel histogram shifting of noncircular type (NHS) and circular type (CHS). Third, we prevent the generation of false start/stop codons by verifying whether a start/stop codon is included in an integer value or between adjacent integer values. The results of our experiments confirmed that the NHSand CHS-based methods have higher watermark capacities than conventional methods in terms of supplementary data used for decoding. Moreover, unlike conventional methods, our methods do not generate false start/stop codons.

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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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Reversible Watermarking Based on Invariant Image Classification and Dynamic Histogram Shifting

Cited by 199 publications

References 17 publications

A 3D Steganalytic Algorithm and Steganalysis-Resistant Watermarking

A 3D Steganalytic Algorithm and Steganalysis-Resistant Watermarking

Reversible Data Hiding for DNA Sequence Using Multilevel Histogram Shifting

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

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