Secure Digital Image Watermarking Technique Based on ResNet-50 Architecture

作     者：Satya Narayan Das Mrutyunjaya Panda 

作者机构：Department of Computer Science and EngineeringGIET UniversityGunupurOdisha765022India Department of Computer Science and ApplicationsUtkal UniversityBhubaneswarOdisha751004India 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2024年第39卷第6期

页      面：1073-1100页

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Image watermarking quantum logistics Rivest-Shamir-Adleman(RSA) Secure Hash(SHA-3) Lifting Wavelet Transformation(LWT) ResNet-50 deep learning secure communication 

摘      要：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 *** research primarily focuses on deep learning-based approaches to improve the quality of watermarked images,but they have some *** overcome this,the deep learning digital image watermarking model with highly secure algorithms is proposed to secure the digital ***,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 *** 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 *** 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 *** 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 ***,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 *** integrating RSA and SHA-3 algorithms,the proposed model demonstrates improved imperceptibility,robustness,and accuracy in watermark extraction compared to traditional *** achieves a Peak Signal-to-Noise Ratio(PSNR)of 49.83%,a Structural Simi