Mitigating cloud vulnerabilities using a blockchain platform

Abstract

Content-Based Image Retrieval (CBIR) has become a critical technology for efficiently searching and retrieving images from large datasets based on their visual content. Traditional CBIR systems, which rely on low-level features like color, texture, and shape, often struggle with semantic gaps and scalability issues. With the rapid advancements in deep learning and cloud computing, there is a growing need to enhance CBIR performance for real-world applications. In order to tackle the issues, an enhanced CBIR process leveraging advanced neural networks, particularly Convolutional Neural Networks (CNNs), Siamese Networks, and attention mechanisms is proposed. It integrates multimodal data, including text and audio, to improve retrieval accuracy. Additionally, cloud-based infrastructure is employed to support large-scale image processing, enabling faster retrieval times and real-time performance. Edge computing techniques are also incorporated to reduce latency in applications requiring immediate responses. The proposed model demonstrates significant improvements in retrieval accuracy and efficiency compared to traditional CBIR methods. Deep learning models, particularly CNNs with transfer learning and attention mechanisms, effectively capture high-level semantic features. The integration of cloud infrastructure enhances scalability and real-time processing capabilities, while multimodal retrieval improves search relevance. The use of explainable AI techniques adds transparency to the decision-making process, increasing user trust. Hence, the advanced neural networks, coupled with cloud and edge computing, can significantly optimize CBIR systems, making them more robust, scalable, and applicable to a wide range of industries such as healthcare, security, and e-commerce.