A Novel AI-Driven Model for Reversible Image Transformation by Optimized Secure Data Embedding in Cloud Networks

This research article presents a pioneering approach to address the critical challenge of secure reversible data embedding in encrypted images within cloud storage networks. The proposed concept centers around the development of an advanced artificial intelligence (AI)-based optimization algorithm designed to identify high-capacity predictors for efficient data embedding, all while preserving the integrity of encrypted images. Our algorithm begins by creating trial data, embedding it into encrypted training images using a prediction error expansion technique. The process is repeated across various predictors to determine the one yielding the maximum embedding rate. The chosen predictor becomes the label for training a Convolutional Neural Network (CNN). Unlike conventional methods, our algorithm leverages the trained CNN to predict the optimal embedding predictor for a given test image, eliminating the need for the resource-intensive trial data embedding during testing. The evaluation on benchmark datasets, namely BossBase and BOWS-2, demonstrates the algorithm's efficacy This research significantly advances the field of reversible data embedding by introducing a state-of-the-art AI-optimized algorithm, showcasing its potential to enhance the security and efficiency of data embedding in cloud network environments. The concept's innovation lies in its ability to achieve robust performance without compromising the quality of encrypted images, thereby contributing to the ongoing discourse on secure and efficient data transformation in cloud-based platforms.