A robust SVD-based image watermarking using a multi-objective particle swarm optimization

Loukhaoukha, Khaled; Nabti, Makram; Zebbiche, Khalil

doi:10.2478/s11772-014-0177-z

Cited by 33 publications

(16 citation statements)

References 39 publications

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“…Lenarczyk and Piotrowski [18] preferred watermark embedding and extraction in YCbCr Color model than in RGB Model. Loukhaoukha et al [19] compressed the image using lifting wavelet transform and SVD watermarking combined with multiobjective PSO was used. Verma and Jha [20] discussed the algorithm of embedding binary watermark using CH3 subband coefficients.…”

Section: Related Workmentioning

confidence: 99%

Comparison of DCT, SVD and BFOA based multimodal biometric watermarking systems

Nair

Aruna

2015

Alexandria Engineering Journal

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Digital image watermarking is a major domain for hiding the biometric information, in which the watermark data are made to be concealed inside a host image imposing imperceptible change in the picture. Due to the advance in digital image watermarking, the majority of research aims to make a reliable improvement in robustness to prevent the attack. The reversible invisible watermarking scheme is used for fingerprint and iris multimodal biometric system. A novel approach is used for fusing different biometric modalities. Individual unique modalities of fingerprint and iris biometric are extracted and fused using different fusion techniques. The performance of different fusion techniques is evaluated and the Discrete Wavelet Transform fusion method is identified as the best. Then the best fused biometric template is watermarked into a cover image. The various watermarking techniques such as the Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD) and Bacterial Foraging Optimization Algorithm (BFOA) are implemented to the fused biometric feature image. Performance of watermarking systems is compared using different metrics. It is found that the watermarked images are found robust over different attacks and they are able to reverse the biometric template for Bacterial Foraging Optimization Algorithm (BFOA) watermarking technique. ª 2015 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Section: Related Workmentioning

confidence: 99%

Comparison of DCT, SVD and BFOA based multimodal biometric watermarking systems

Nair

Aruna

2015

Alexandria Engineering Journal

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“…PSO had been combined with SVD watermarking methods as in [8,9,41,42]. The work in [41] proposed a watermarking algorithm that initially decomposes the host image into sub-bands using DWT; afterwards, singular values of each sub-band of the host image are modified with the scaling factors to embed watermark.…”

Section: Related Workmentioning

confidence: 99%

“…One quantization parameter is determined by exploiting the characteristics of HVS, and the other quantization parameter is optimized through PSO algorithm. Loukhaoukha et al [42] proposed an optimal image watermarking scheme using multi-objective particle swarm optimization (MOPSO) and SVD in wavelet domain. The singular values of the specific subband are modified by multiple scaling factors (MSF) to embed the singular values of watermark image.…”

Section: Related Workmentioning

confidence: 99%

“…The singular values of the specific subband are modified by multiple scaling factors (MSF) to embed the singular values of watermark image. The work in [42] utilizes the PSO to obtain optimum multiple scaling factors. Soliman et al [9] provide an enhancement on the work in [8] where QPSO is used instead of classical PSO to optimize SVD-based watermark parameters.…”

Section: Related Workmentioning

confidence: 99%

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An adaptive watermarking approach based on weighted quantum particle swarm optimization

Soliman

Hassanien

Onsi

2015

Neural Comput & Applic

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In this paper, we propose a novel optimal singular value decomposition (SVD)-based image watermarking approach that uses a new combination of weighted quantum particle swarm optimization (WQPSO) algorithm and a human visual system (HVS) model for both the hybrid discrete wavelet transform and discrete cosine transform (DCT). The proposed SVD-based watermarking approach initially decomposes the host image into subbands; afterwards, singular values of the DCT of the lower sub-band of the host image are quantized using a set of optimal quantization steps deduced from a combination of the WQPSO algorithm and the HVS model. To evaluate the performance of the proposed approach, we present tests on different images. The experimental results show that the proposed approach yields a watermarked image with good visual definition; at the same time, the embedded watermark was robust against a wide variety of common attacks, including JPEG compression, Gaussian noise, salt and pepper noises, Gaussian filters, median filters, image cropping, and image scaling. Moreover, the results of various experimental analyses demonstrated the superiority of the WQPSO approach over other optimization techniques, including classical PSO and QPSO in terms of local convergence speed, resulting in a better balance between global and local searches of the watermarking algorithm.

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“…SVD is widely used in many fields, such as, image processing [15], watermaking [16,17], network analysis [18], and financial analysis, etc. In this section, an application of SVD in finance is introduced.…”

Section: Applicationmentioning

confidence: 99%

Adjustable Robust Singular Value Decomposition: Design, Analysis and Application to Finance

Wang

2017

Data

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Abstract:The Singular Value Decomposition (SVD) is a fundamental algorithm used to understand the structure of data by providing insight into the relationship between the row and column factors. SVD aims to approximate a rectangular data matrix, given some rank restriction, especially lower rank approximation. In practical data analysis, however, outliers and missing values maybe exist that restrict the performance of SVD, because SVD is a least squares method that is sensitive to errors in the data matrix. This paper proposes a robust SVD algorithm by applying an adjustable robust estimator. Through adjusting the tuning parameter in the algorithm, the method can be both robust and efficient. Moreover, a sequential robust SVD algorithm is proposed in order to decrease the computation volume in sequential and streaming data. The advantages of the proposed algorithms are proved with a financial application.

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A robust SVD-based image watermarking using a multi-objective particle swarm optimization

Cited by 33 publications

References 39 publications

Comparison of DCT, SVD and BFOA based multimodal biometric watermarking systems

Comparison of DCT, SVD and BFOA based multimodal biometric watermarking systems

An adaptive watermarking approach based on weighted quantum particle swarm optimization

Adjustable Robust Singular Value Decomposition: Design, Analysis and Application to Finance

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