Image watermarking using tree-based spatial-frequency feature of wavelet transform

Zhang, Xu Dong; Feng, Jian; Lo, Kwok-Tung

doi:10.1016/s1047-3203(03)00047-6

Cited by 32 publications

(17 citation statements)

References 14 publications

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“…Fig. 6 shows the comparison results of the BWIW technique, Joo's [1], and Wang's methods [3] for the transparency. For the case of attack free, Fig.…”

Section: Resultsmentioning

confidence: 99%

“…During watermark extraction, the BWIW technique uses the Trained ANN (TANN) to estimate the watermark without the original image. In contrast, some methods require the original image to be applied in the JND-profile calculation during watermark extraction [3,5]. Observing the computer simulations, the BWIW technique definitely outperforms other proposed methods for both the transparency and the robustness.…”

Section: Introductionmentioning

confidence: 90%

“…First, the JND profile of a wavelettransformed image is not used in the design of their techniques during watermark embedding [1,2]. Second, the original images are required for the calculation of the JND profile of the images during watermark extraction [3,4]. In [5,6], Wang et al proposed a watermarking method, based on the JND profile, for JPEG and JPEG2000 images.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Blind Wavelet-based Image Watermarking Based on HVS and Neural Networks

Tsai¹,

Liu²,

Wang³

2006

Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

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This work proposes a Blind Wavelet-based Image Watermarking (BWIW) technique, based on the human visible system (HVS) model and neural networks, for image copyright protection. A characteristic of the HVS, the Just Noticeable Difference (JND) profile, is employed in the watermark embedding so that the BWIW technique can make the watermark further imperceptible. The technique is developed in the wavelet domain. While embedding a watermark in an image, it adds the adaptive strengths to the wavelet coefficients of the image according to the JND thresholds. Moreover, an artificial neural network (ANN) is used to memorize the relationships between the wavelet version of an original image and its watermarked image. An advantage of the BWIW technique is that it utilizes the trained ANN to estimate the watermark without the original image to be applied in the calculation of the JND profile of the image. Finally, computer simulations demonstrate that both the transparency and the robustness of the BWIW technique are better than that of other proposed methods.

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“…Fig. 6 shows the comparison results of the BWIW technique, Joo's [1], and Wang's methods [3] for the transparency. For the case of attack free, Fig.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Blind Wavelet-based Image Watermarking Based on HVS and Neural Networks

Tsai¹,

Liu²,

Wang³

2006

Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

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“…In a number of wavelet-based watermarking schemes, the Human Visual System (HVS) is employed to determine the modifiable wavelet coefficients to embed the watermark which on modification is imperceptible or transparent to human eyes [14], [15], [16]. In general, while the discernible quality of the content is at a sufficient level, the watermark embedded into the original data is allowed to reside.…”

Section: Introductionmentioning

confidence: 99%

An Effective Wavelet-Based Watermarking Scheme Using Human Visual System for Protecting Copyrights of Digital Images

Reddy¹,

Varadarajan²

2010

IJCEE

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Abstract-The modern era of information technology facilitates easy duplication, manipulation and distribution of digital data. This has demanded the certainty for effectively safe guarding the rightful ownership of digital images. Copyright protection and content authentication of digital images have emerged as an imperative concern to content owners and distributors. Digital watermarking provided a potential solution to this issue. The watermarking scheme must possess the characteristics of robustness and imperceptibility to ensure efficient copyright protection. An effective copyright protection is achieved by the incorporation of Human Visual System (HVS) models into watermarking scheme. Recently, interest in watermarking researches are chiefly fascinated by wavelet domain based watermarking schemes. In our earlier work, we have presented an imperceptible and efficient wavelet-based watermarking scheme for protecting the copyrights of images. Here, we have offered a proficient watermarking scheme by effecting some changes to our earlier work. The presented scheme for watermarking incorporates the HVS models into watermark embedding. The biorthogonal wavelet transform with lifting scheme is utilized in the watermarking. The image components for embedding are selected based on the entropy masking. A random matrix is generated on the basis of a key image and is engaged in both embedding and extraction processes. The correlation coefficient computation is made use of in the extraction of watermark. The experimental results illustrate the robustness and imperceptibility of the proposed approach.

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“…Discrete wavelet transform is a common technique to transform an image from spatial domain to frequency domain due to its computation efficiency. Some waveletbased watermarking schemes utilize human visual system (HVS) to determine which wavelet coefficients can be modified to embed the watermark so that the change is imperceptible, or transparent, to human eyes [1][2][3]5]. However, the formulas and computation for finding transparent coefficients are usually complex.…”

Section: Introductionmentioning

confidence: 99%

An Imperceptible Watermarking Scheme Using Variation and Modular Operations

Hsu

2008

2008 International Conference on Multimedia and Ubiquitous Engineering (Mue 2008)

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The aim of this paper is to propose an imperceptible wavelet-based watermarking scheme. In our scheme, we use variation to define the degree of transparence of coefficients of LL band and further determine transparent coefficients of HL3 and LH3 bands. Then, the watermark is embedded into those coefficients. In addition, the modular operation is utilized during watermark embedding. In the experimental results, we simulate some common attacks on the watermarked image. It is shown that our scheme is robust against most attacks; moreover, the watermarked image is highly imperceptible.

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Image watermarking using tree-based spatial-frequency feature of wavelet transform

Cited by 32 publications

References 14 publications

Blind Wavelet-based Image Watermarking Based on HVS and Neural Networks

Blind Wavelet-based Image Watermarking Based on HVS and Neural Networks

An Effective Wavelet-Based Watermarking Scheme Using Human Visual System for Protecting Copyrights of Digital Images

An Imperceptible Watermarking Scheme Using Variation and Modular Operations

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