ADRWISD-ADVANCED DCNN ROBUSTNESS ARCHITECTURES FOR WATERMARK IMAGES SMOOTHENING AND DENOISING

Abstract

Watermarking has emerged as a crucial technique for ensuring the integrity and authenticity of digital images, especially in an era of rampant digital content sharing. However, watermark images are susceptible to distortion and loss of fidelity due to various image processing techniques, particularly smoothening and denoising. This study introduces ADRWISD, a novel approach that leverages advanced Deep Convolutional Neural Network (DCNN) architectures to enhance the robustness of watermark images during smoothening and denoising. The proposed ADRWISD framework addresses key challenges in watermark image processing by introducing a structure-aware and scale-adaptive approach to image smoothening. This approach balances the need for noise reduction and artifact removal with the preservation of embedded watermarks. Using sophisticated DCNN architectures, ADRWISD maintains the structural integrity of watermark images while achieving significant denoising and smoothening, thus ensuring that critical information remains intact. The framework employs DCNN architectures that dynamically adjust to varying image scales and structures, providing enhanced smoothening without compromising watermark integrity. In conclusion, ADRWISD represents a significant advancement in the field of digital watermarking and image processing. By integrating advanced DCNN architectures with adaptive techniques, this framework offers a robust solution for smoothening and denoising watermark images while preserving critical information. The study provides a pathway for future research in creating more resilient and efficient methods for digital image processing in various applications, including digital forensics, content verification, and copyright protection.