A Genetic Algorithm Optimization Technique for Multiwavelet-Based Digital Audio Watermarking

Kumsawat, Prayoth

doi:10.1155/2010/471842

Cited by 23 publications

(29 citation statements)

References 17 publications

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“…После процесса оптимизации вектора коэф-фициентов размытости ядра при помощи метода деформируемого многогранника будет проведена оптимизация вектора коэффициентов размытости ядра при помощи генетического алгоритма [10].…”

Section: рис 4 зависимость среднеквадратичной ошибки от коэффициентunclassified

Optimization of the Core Smooth Coefficient in the Problem of the Nonparametric Identification

Михов¹,

Ivanov²

2018

S&T

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ω -каналы связи, соответ-ствующие различным переменным и включающие в себя средства контроля, приборы для измерения наблюдаемых переменных; µ t , u t , x t , ω t -означа-ет измерение в дискретное время; µ(t), u(t), x(t), ω(t), h µ (t), h u (t), h x (t), h ω (t) -случайные помехи из-мерений соответствующих переменных процесса. Процессы с дискретным снятием измерений часто встречаются в различных технических областях. С помощью подобных моделей проводится диа-гностирование компонентов электроники (диодов, резисторов и т.д.) на предмет брака. Подобная диагностика проводится при анализе компонентов элементной базы, используемых для сборки кос-мических летательных аппаратов.КОСМИЧЕСКОЕ ПРИБОРОСТРОЕНИЕ

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Section: рис 4 зависимость среднеквадратичной ошибки от коэффициентunclassified

Optimization of the Core Smooth Coefficient in the Problem of the Nonparametric Identification

Михов¹,

Ivanov²

2018

S&T

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“…(Storn and Price 1997), and genetic algorithm (GA) (Kumsawat 2010) have been demonstrated to be very effective in solving conflicting requirements of watermarking. All these techniques intend to work on minimizing the trade-off between the two mandatory objectives of watermarking which are imperceptibility of the embedded watermark and robustness of the embedding scheme.…”

Section: Motivationsmentioning

confidence: 99%

Ownership protection of outsourced biomedical time series data based on optimal watermarking scheme in data mining

Pham

Tran

2017

AJIS

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In the biomedical and healthcare fields, the ownership protection of the outsourced data is becoming a challenging issue in sharing the data between data owners and data mining experts to extract hidden knowledge and patterns. Watermarking has been proved as a rightprotection mechanism that provides detectable evidence for the legal ownership of a shared dataset, without compromising its usability under a wide range of data mining for digital data in different formats such as audio, video, image, relational database, text and software. Time series biomedical data such as Electroencephalography (EEG) or Electrocardiography (ECG) is valuable and costly in healthcare, which need to have owner protection when sharing or transmission in data mining application. However, this issue related to kind of data has only been investigated in little previous research as its characteristics and requirements. This paper proposes an optimized watermarking scheme to protect ownership for biomedical and healthcare systems in data mining. To achieve the highest possible robustness without losing watermark transparency, Particle Swarm Optimization (PSO) technique is used to optimize quantization steps to find a suitable one. Experimental results on EEG data show that the proposed scheme provides good imperceptibility and more robust against various signal processing techniques and common attacks such as noise addition, low-pass filtering, and resampling.

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“…This analysis shows that the HPSO-based technique is more reliable compared to other techniques, but it is more complex. The GA has been used to embed the image watermark in audio signals [17]. It is used to determine the quantization step for embedding permuted images into selected coefficients of the audio signal.…”

Section: Introductionmentioning

confidence: 99%

A particle swarm optimization and block-SVD-based watermarking for digital images

Thakkar¹,

Srivastava²

2017

Turk J Elec Eng & Comp Sci

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Abstract:The major issues in most watermarking schemes are security, reliability, and robustness against attacks. To achieve these objectives in a watermarking algorithm, the selection of a scale factor to embed the watermark into the host image is a challenging problem. In this paper, a block singular value decomposition (SVD)-based reliable, robust, secure, and fast watermarking scheme is proposed that uses particle swarm optimization (PSO) in the selection of the scale factor. SVD is applied here on the nonoverlapping blocks of LL wavelet subbands. Selected singular values of these blocks are modified with the pixel values of the watermark image. Selected locations of these singular values increase security. In addition, the scale factor using PSO for embedding the watermark increases the robustness and imperceptibility of the proposed scheme. Direct embedding of the watermark image into the host image and the use of block-SVD makes the scheme faster. Comparative analysis with an existing algorithm shows that the proposed technique performs well during most types of noise attacks and removes the diagonal line problem present in the extracted watermark image.

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A Genetic Algorithm Optimization Technique for Multiwavelet-Based Digital Audio Watermarking

Cited by 23 publications

References 17 publications

Optimization of the Core Smooth Coefficient in the Problem of the Nonparametric Identification

Optimization of the Core Smooth Coefficient in the Problem of the Nonparametric Identification

Ownership protection of outsourced biomedical time series data based on optimal watermarking scheme in data mining

A particle swarm optimization and block-SVD-based watermarking for digital images

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