Enhancing Image Watermarking Efficiency through Advanced Deep Learning: A Novel Approach with Modified Res-Ception Blocks and Practical Applications in Modern Scenarios

Abstract

In recent years, digital media has become a prevalent form of information exchange, necessitating robust measures for privacy, data safety, and copyright protection. Image watermarking is one of the tools that can be employed for these tasks. Research on blind image watermarking techniques is lacking, and there is a gap between theoretical findings and practical implementations to address contemporary issues such as photo metadata privacy, AI generated image recognition, etc. The proposed research introduces a revolutionary deep learning-based image watermarking approach in order to address these issues. This scheme is designed to overcome the contemporary challenges related to image watermarking by being blind, efficient and robust. It also has an improved PSNR and beats state-of-the-art in various attacks. The model outperforms existing deep learning frameworks in terms of fidelity. All these are achieved by creating a modified and much more efficient form of Inception Resnet block, coined ResCeption Block, for use in effective learning of watermarking networks. The research also presents an application of watermarking in the identification of AI generated images and proposes a novel and robust algorithm for metadata encryption of digital images and enforce digital media security. Rigorous testing has been performed on benchmark datasets, and comparisons have been done against state-of-the-art and a PSNR of 43.12 dB has been achieved.