Dynamic and Integrated Security Model in Inter Cloud for Image Classification

Abstract

Cloud computing has transformed software and database accessibility, utilizing the Internet and server hosting. However, security risks arise, including malware attacks and website hacking. To address these challenges, deep learning models like ResNet50 have been developed. Trained on encrypted images, ResNet50 enhances the speed and accuracy of image recognition, enabling the identification of hidden data without decryption. Despite inter-cloud communication issues, cloud servers prioritize data security, user privacy, and integrity maintenance. The ResNet50 model exhibits impressive performance, achieving 99.5%accuracy and precision-recall scores of 99.5% and 99.5% using the ImageNet Dataset. Cloud computing offers significant advantages, but data security remains a critical concern. Encrypted image recognition powered by deep learning models offers efficient and private solutions. Cloud providers continually strive to improve inter-cloud communication, ensuring comprehensive protection for data and system integrity. The remarkable capabilities of ResNet50 highlight its potential in encrypted image analysis tasks.