IDedupNet: A MobileNetV3-Based Deep Learning Framework for Efficient Image Deduplication in Cloud Computing Environments

Abstract

Image deduplication is becoming increasingly important for cloud storage infrastructures to handle the increasing amount of multimedia material. Through increased storage efficiency, effective picture deduplication may optimize resources and save expenses. It also improves performance by facilitating quicker access, utilizing less bandwidth, and enhancing data integrity. Although heuristic-based classical deduplication techniques work well in various storage infrastructures, they cannot keep up with the dynamic nature of cloud storage resources. This study presents IDedupNet, a revolutionary DL-based framework that improves infrastructure performance in cloud computing by efficiently detecting duplicate and near-duplicate photos. Our approach uses deep learning for picture encoding and deduplication, allowing it to manage duplicate photos in highly dynamic contexts efficiently. Additionally, we provide a Learning-Based Image Deduplication (LBID) approach that improves deduplication performance by extending the use of the IDedupNet model. Our suggested deep learning model has considerable advantages over current models, regularly beating them and obtaining a high accuracy of 98.68% on benchmark datasets, which increases the model's credibility. The underlying technique and deep learning framework may be easily integrated into real-time cloud storage systems to increase customer satisfaction and infrastructure efficiency.