In today’s world of massive data and interconnected networks,it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital content.Existing research primarily focuse...In today’s world of massive data and interconnected networks,it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital content.Existing research primarily focuses on deep learning-based approaches to improve the quality of watermarked images,but they have some flaws.To overcome this,the deep learning digital image watermarking model with highly secure algorithms is proposed to secure the digital image.Recently,quantum logistic maps,which combine the concept of quantum computing with traditional techniques,have been considered a niche and promising area of research that has attracted researchers’attention to further research in digital watermarking.This research uses the chaotic behaviour of the quantum logistic map with Rivest–Shamir–Adleman(RSA)and Secure Hash(SHA-3)algorithms for a robust watermark embedding process,where a watermark is embedded into the host image.This way,the quantum chaos method not only helps limit the chance of tampering with the image content through reverse engineering but also assists in maintaining a high level of imperceptibility and strong robustness with efficient extraction or detection of watermark images.Lifting Wavelet Transformation(LWT)is a potential and computationally efficient version of traditional Discrete Wavelet Transform(DWT)where the host image is divided into four sub-bands to offer a multi-resolution view of an image with greater flexibility in watermarking methodologies.Furthermore,considering the robustness against attacks,a pre-trained Residual Neural Network(ResNet-50),a convolutional neural network with 50 layers deep,is used to better learn the complex features and efficiently extract the watermark from the image.By integrating RSA and SHA-3 algorithms,the proposed model demonstrates improved imperceptibility,robustness,and accuracy in watermark extraction compared to traditional methods.It achieves a Peak Signal-to-Noise Ratio(PSNR)of 49.83%,a Structural Similarity Index Measure(SSIM)of 0.98,and a Number of Pixels Change Rate(NPCR)of 99.79%,respectively.These results reflect the model’s effectiveness in delivering superior quality and security.Consequently,our proposed approach offers accurate results,exceptional invisibility,and enhanced robustness compared to the existing digital image watermarking techniques.展开更多
The molecular electronegativity interaction vector （MEIV） was used to describe the molecular structure of 30 selected esters. Two excellent QSTR models were built up by using multiple linear regression （MLR） and p...The molecular electronegativity interaction vector （MEIV） was used to describe the molecular structure of 30 selected esters. Two excellent QSTR models were built up by using multiple linear regression （MLR） and partial least-squares regression （PLS）. The correlation coefficients （R） of the two models were 0.945 and 0.941, respectively. The models were evaluated by performing the cross validation with the leave-one-out （LOO） procedure. The cross-verification correlation coefficients （RCV） of the two models were 0.921 and 0.919, respectively. The results showed that the models constructed in this work could provide estimation stability and favorable predictive ability.展开更多
文摘In today’s world of massive data and interconnected networks,it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital content.Existing research primarily focuses on deep learning-based approaches to improve the quality of watermarked images,but they have some flaws.To overcome this,the deep learning digital image watermarking model with highly secure algorithms is proposed to secure the digital image.Recently,quantum logistic maps,which combine the concept of quantum computing with traditional techniques,have been considered a niche and promising area of research that has attracted researchers’attention to further research in digital watermarking.This research uses the chaotic behaviour of the quantum logistic map with Rivest–Shamir–Adleman(RSA)and Secure Hash(SHA-3)algorithms for a robust watermark embedding process,where a watermark is embedded into the host image.This way,the quantum chaos method not only helps limit the chance of tampering with the image content through reverse engineering but also assists in maintaining a high level of imperceptibility and strong robustness with efficient extraction or detection of watermark images.Lifting Wavelet Transformation(LWT)is a potential and computationally efficient version of traditional Discrete Wavelet Transform(DWT)where the host image is divided into four sub-bands to offer a multi-resolution view of an image with greater flexibility in watermarking methodologies.Furthermore,considering the robustness against attacks,a pre-trained Residual Neural Network(ResNet-50),a convolutional neural network with 50 layers deep,is used to better learn the complex features and efficiently extract the watermark from the image.By integrating RSA and SHA-3 algorithms,the proposed model demonstrates improved imperceptibility,robustness,and accuracy in watermark extraction compared to traditional methods.It achieves a Peak Signal-to-Noise Ratio(PSNR)of 49.83%,a Structural Similarity Index Measure(SSIM)of 0.98,and a Number of Pixels Change Rate(NPCR)of 99.79%,respectively.These results reflect the model’s effectiveness in delivering superior quality and security.Consequently,our proposed approach offers accurate results,exceptional invisibility,and enhanced robustness compared to the existing digital image watermarking techniques.
基金supported by the Youth Foundation of Education Bureau, Sichuan Province (09ZB036)Technology Bureau, Sichuan Province (2006j13-141)
文摘The molecular electronegativity interaction vector （MEIV） was used to describe the molecular structure of 30 selected esters. Two excellent QSTR models were built up by using multiple linear regression （MLR） and partial least-squares regression （PLS）. The correlation coefficients （R） of the two models were 0.945 and 0.941, respectively. The models were evaluated by performing the cross validation with the leave-one-out （LOO） procedure. The cross-verification correlation coefficients （RCV） of the two models were 0.921 and 0.919, respectively. The results showed that the models constructed in this work could provide estimation stability and favorable predictive ability.
