Digital watermark extraction using support vector machine with principal component analysis based feature reduction

Verma, Vivek Singh; Jha, Rajib Kumar; Ojha, Aparajita

doi:10.1016/j.jvcir.2015.06.001

Cited by 55 publications

(39 citation statements)

References 38 publications

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“…Firms' business methods are changing from product-centric to (1) Singular value decomposition: This method has a good effect when the data dimension is high, and it is often used as a preprocessing method to realize the convergence of fuzzy rough reduction in high-dimensional data sets; but the calculation cost of this method is high [13,14]; (2) The principal component analysis is reduced, and the mutual influence between evaluation indexes is eliminated by replacing the original variables with several principal components with larger contributions. This study is necessary to delete irrelevant or unimportant attributes to eliminate the interference of irrelevant features when using the data with higher dimensions [15]; (3) Effective feature extraction in deep learning: Data-driven deep learning analysis has been developed and applied in many fields. The ability to fit and extract features has been improved by combining multiple processing layers in a variety of data analysis tasks [16]; (4) The attribute reduction of rough set theory is an extension of the theory of modeling ambiguity and imprecision [17].…”

Section: Introductionmentioning

confidence: 99%

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Zhou

Tan

et al. 2020

Symmetry

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The neighborhood rough set theory was adopted for attributes reduction and the weight distribution of condition attributes based on the concept of importance level. Smart elderly care coverage rate is low in China. A decisive role in the adoption of smart elderly care is still a problem that needs to be addressed. This study contributes to the adoption of smart elderly care was selected as the decision attribute. The remaining attributes are used as conditional attributes and the multi-level symmetric attribute set for assessing acceptance of smart elderly care. Prior studies are not included smart elderly care adoption attributes in multi-levels; hence, this problem needs to be addressed. The results of this study indicate that the condition attribute of gender has the greatest influence on the decision attribute. The condition attribute of living expenses for smart elderly care has the second largest impact on decision attribute. Children’s support for the elderly decency of the novel elderly care system and the acceptance of non-traditional elderly care methods belong to the primary condition attribute of traditional concept. The result indicates traditional concepts have a certain impact on the adoption of smart elderly care and a condition attribute of residence also has a slight influence on the symmetric decision attribute. The sensitivity analysis shows the insights for uncertainties and provides as a basis for the analysis of the attributes in the smart elderly care service adoption.

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Section: Introductionmentioning

confidence: 99%

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Zhou

Tan

et al. 2020

Symmetry

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“…Verma et al [33] embedded a binary watermark into the low-pass sub-band of LWT of the original cover image by a quantization process. They proposed a watermark extraction method based on SVM and Principal Component Analysis (PCA).…”

Section: Related Workmentioning

confidence: 99%

Wavelet-Hadamard based blind image watermarking using genetic algorithm and decision tree

Houby

Yassin

2020

Multimed Tools Appl

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Recently the active use of Internet and multimedia technologies has increased the violation of copyright. Digital watermarking has a crucial role in the field of multimedia copyright protection. In this paper, an optimized image watermarking technique based on Discrete Wavelet Transform (DWT) and Hadamard transform is proposed. Genetic Algorithm (GA) is an optimizing technique used to satisfy the tradeoff between robustness and imperceptibility. Blind property is conducted using the estimation capability of Decision Tree (DT). First the host image is transformed using second level DWT, then Hadamard transform is applied on selected DWT sub-bands. Adaptive multiple strength values are computed using GA. The watermark is successfully extracted using the trained DT which estimates the original coefficients needed for the extraction process without the need for the original host image. The proposed technique is evaluated against several types of attacks: compression, median filtering, salt and pepper noise, histogram equalization, blurring, scaling, painting, and cropping. Experimental results show that the proposed watermarking technique is robust against these attacks while keeping good imperceptibility. The proposed technique outperforms the compared techniques according to robustness, imperceptibility, and capacity.

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“…To prove the superiority and the robustness advantages of our scheme a comparative study with state of the art works is indispensable. In fact, we compared the robustness of our scheme with six robust watermarking schemes [26,27,35,[37][38][39]. In order to carry out a fair comparison, the same insertion parameters (PSNR, watermark size) and attacks are used in this comparison.…”

Section: Comparative Studymentioning

confidence: 99%

A Robust Watermarking Scheme Based on the Mean Modulation of DWT Coefficients

Kricha

Sakly

2018

Security and Communication Networks

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In an endeavor to deal with image copyright infringement, robust watermarking approaches are commonly used. However, most of the existing approaches either present a limited robustness or rely on highly computational algorithms, thereby limiting the efficiency of these solutions. In this paper, a novel blind and robust watermarking method is presented. First, the vertical and horizontal subbands coefficients, resulting from the wavelet transformation, are scrambled using a chaotic sequence and then gathered into individual blocks. Next, the mean value of each block is modulated according to watermark bit. At the extraction stage, based on the sign of the blocks’ mean, a blind watermark extractor is suggested. The imperceptibility, security, complexity, and robustness of the proposed approach have been evaluated and compared with state of the art solutions. Experimental results prove that the proposed approach successfully satisfies the watermarking requirement and outperforms existing methods against both geometric and signal processing attacks.

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Digital watermark extraction using support vector machine with principal component analysis based feature reduction

Cited by 55 publications

References 38 publications

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Wavelet-Hadamard based blind image watermarking using genetic algorithm and decision tree

A Robust Watermarking Scheme Based on the Mean Modulation of DWT Coefficients

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