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A Novel Color Image Watermarking Method with adaptive scaling factor Using Similarity-Based Edge Region

Computer Systems Science & Engineering 2023年第10期47卷 55-77页

作者： Kali Gurkahraman Rukiye Karakis Hidayet Takci Faculty of Engineering Department of Computer EngineeringSivas Cumhuriyet UniversitySivas58140Turkey Faculty of Technology Department of Software EngineeringSivas Cumhuriyet UniversitySivas58140Turkey

This study aimed to deal with three challenges:robustness,imperceptibility,and capacity in the image watermarking field.To reach a high capacity,a novel similarity-based edge detection algorithm was developed that finds more edge points than traditional techniques.The colored watermark image was created by inserting a randomly generated message on the edge points detected by this algorithm.To ensure robustness and imperceptibility,watermark and cover images were combined in the high-frequency subbands using Discrete Wavelet Transform and Singular Value Decomposition.In the watermarking stage,the watermark image was weighted by the adaptive scaling factor calculated by the standard deviation of the similarity image.According to the results,the proposed edge-based color image watermarking technique has achieved high payload capacity,imperceptibility,and robustness to all attacks.In addition,the highest performance values were obtained against rotation attack,to which sufficient robustness has not been reached in the related studies.

关键词： Image watermarking edge detection discrete wavelet transform singular value decomposition adaptive scaling factor

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Modified Multifidelity Surrogate Model Based on Radial Basis Function with adaptive Scale factor

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Chinese Journal of Mechanical Engineering 2022年第4期35卷 109-123页

作者： Yin Liu Shuo Wang Qi Zhou Liye Lv Wei Sun Xueguan Song School of Mechanical Engineering Dalian University of Technology School of Aerospace Engineering Huazhong University of Science and Technology School of Mechanical Engineering and Automation Zhejiang Sci-Tech University

Multifidelity surrogates(MFSs) replace computationally intensive models by synergistically combining information from different fidelity data with a significant improvement in modeling efficiency. In this paper, a modified MFS(MMFS) model based on a radial basis function(RBF) is proposed, in which two fidelities of information can be analyzed by adaptively obtaining the scale factor. In the MMFS, an RBF was employed to establish the low-fidelity model.The correlation matrix of the high-fidelity samples and corresponding low-fidelity responses were integrated into an expansion matrix to determine the scaling function parameters. The shape parameters of the basis function were optimized by minimizing the leave-one-out cross-validation error of the high-fidelity sample points. The performance of the MMFS was compared with those of other MFS models(MFS-RBF and cooperative RBF) and single-fidelity RBF using four benchmark test functions, by which the impacts of different high-fidelity sample sizes on the prediction accuracy were also analyzed. The sensitivity of the MMFS model to the randomness of the design of experiments(DoE) was investigated by repeating sampling plans with 20 different DoEs. Stress analysis of the steel plate is presented to highlight the prediction ability of the proposed MMFS model. This research proposes a new multifidelity modeling method that can fully use two fidelity sample sets, rapidly calculate model parameters, and exhibit good prediction accuracy and robustness.

关键词： Multi-fidelity surrogate RBF adaptive scaling factor LOOCV Expansion matrix

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Modifed Multifdelity Surrogate Model Based on Radial Basis Function with adaptive Scale factor

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Chinese Journal of Mechanical Engineering 2022年第4期35卷 93-107页

作者： Yin Liu Shuo Wang Qi Zhou Liye Lv Wei Sun Xueguan Song School of Mechanical Engineering Dalian University of TechnologyDalian 116000China School of Aerospace Engineering Huazhong University of Science and TechnologyWuhan 430000China School of Mechanical Engineering and Automation Zhejiang Sci-Tech UniversityHangzhou 310000China

Multifdelity surrogates(MFSs)replace computationally intensive models by synergistically combining information from diferent fdelity data with a signifcant improvement in modeling efciency.In this paper,a modifed MFS(MMFS)model based on a radial basis function(RBF)is proposed,in which two fdelities of information can be analyzed by adaptively obtaining the scale factor.In the MMFS,an RBF was employed to establish the low-fdelity model.The correlation matrix of the high-fdelity samples and corresponding low-fdelity responses were integrated into an expansion matrix to determine the scaling function parameters.The shape parameters of the basis function were optimized by minimizing the leave-one-out cross-validation error of the high-fdelity sample points.The performance of the MMFS was compared with those of other MFS models(MFS-RBF and cooperative RBF)and single-fdelity RBF using four benchmark test functions,by which the impacts of diferent high-fdelity sample sizes on the prediction accuracy were also analyzed.The sensitivity of the MMFS model to the randomness of the design of experiments(DoE)was investigated by repeating sampling plans with 20 diferent DoEs.Stress analysis of the steel plate is presented to highlight the prediction ability of the proposed MMFS model.This research proposes a new multifdelity modeling method that can fully use two fdelity sample sets,rapidly calculate model parameters,and exhibit good prediction accuracy and robustness.

关键词： Multi-fdelity surrogate RBF adaptive scaling factor LOOCV Expansion matrix

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