Robust image watermarking scheme in lifting wavelet domain using GA-LSVR hybridization

Mehta, Rajesh; Rajpal, Navin; Vishwakarma, Virendra P.

doi:10.1007/s13042-015-0329-6

Cited by 45 publications

(18 citation statements)

References 36 publications

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“…Mehta et al [18] proposed a watermarking scheme using combination of the GA and Lagrangian support vector regression (LSVR) in lifting wavelet domain. A binary watermark is embedded in the LL sub-band using fuzzy entropy to select the appropriate blocks for embedding.…”

Section: Related Workmentioning

confidence: 99%

“…This literature covers some of the recent research articles that used different MLTs for copyright protection using watermark. However, the articles Mehta et al [18], Kazemivash et al [10], Kazemivash et al [9], and Tsai and Liu [27] are the closest to the proposed scheme where either optimization techniques are used for scaling factor selection, or MLTs are conducted, and in most of them, the same host images with the same sizes are used.…”

Section: Related Workmentioning

confidence: 99%

“…For each generation, the steps (7-44) will be executed as follows. Lines (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25) illustrate the watermark embedding process. In lines (7)(8)(9)(10)(11)(12), the host image and the watermark image are loaded, then the host image is divided into non overlapping blocks of size 8*8 and a number of Sb blocks are selected randomly for the embedding process with keeping their positions in K as a secret key to be used in the extraction process.…”

Section: Optimization Processmentioning

confidence: 99%

“…The watermark image is divided into four parts [wp 1 , wp 2 , wp 3 , wp 4 ], in which each part will be embedded in its corresponding wavelet sub-band. In lines (13)(14)(15)(16)(17)(18)(19)(20)(21)(22), each selected block is decomposed into its multiresolution representation using second level DWT, then four sub-bands are selected to embed the watermark, which are (HH 21 , LL 22 , HH 23 , LL 24 ). WHT is applied on each sub-band and the watermark bit is embedded in the WHT first coefficient using the generated GA scaling factors.…”

Section: Optimization Processmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Optimization Processmentioning

confidence: 99%

Section: Optimization Processmentioning

confidence: 99%

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

Houby

Yassin

2020

Multimed Tools Appl

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“…Wavelet analysis has been developed relatively fast and its applications have been extended widely in many scientific fields [16][17][18][19][20][21][22][23][24][25]. It has ability of presenting signal local information both in time domain and frequency domain.…”

Section: Wind Speed Data Processing Based On Wavelet Analysismentioning

confidence: 99%

Active power and reactive power dispatch of wind farm based on wavelet learning

Wang

Zhang

et al. 2015

Int. J. Mach. Learn. & Cyber.

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During normal operation, the doubly-fed induction generator (DFIG) generates certain range of reactive power. The DFIG based wind farm can participate in reactive power control of grid as a reactive power supply. In order to get a more stable input wind speed of the DFIG, wavelet multi-resolution analysis method is used. This paper proposes a kind of power dispatch model which considers a learning mechanism of minimum copper loss of all DFIGs in wind farm as an objective function. An active power and reactive power allocation optimization model is established. This power dispatch model makes the working condition of DFIGs and the PCC running in the optimum state. The active power and reactive power generated by wind farm satisfy the power gird requirements of both active power and reactive power. The advantage of the proposed method is verified by a case study which successfully demonstrates the learning mechanism.

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