Secure Digital Image Watermarking Technique Based on ResNet-50 Architecture  

作　　者：Satya Narayan Das Mrutyunjaya Panda 

机构地区：[1]Department of Computer Science and Engineering,GIET University,Gunupur,Odisha,765022,India [2]Department of Computer Science and Applications,Utkal University,Bhubaneswar,Odisha,751004,India

出　　处：《Intelligent Automation & Soft Computing》2024年第6期1073-1100,共28页智能自动化与软计算（英文）

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 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

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

分 类 号：TP391[自动化与计算机技术—计算机应用技术]