Abstract

With the explosive growth of digital media, massive collections of images and videos are being generated every day, creating a strong need for fast and accurate retrieval systems. Existing methods that rely on metadata or manual annotations often fall short—mainly because annotations may be incomplete, inconsistent, or unable to truly represent the visual meaning of the content. In this work, we present an AI-powered image and video retrieval system designed to overcome these challenges. Our approach uses Convolutional Neural Networks (CNNs) to automatically extract rich visual features from individual frames, applies temporal aggregation to capture video-level context, and employs cosine similarity to match content efficiently. Experimental results on benchmark datasets show that our method delivers higher precision and recall than traditional content-based image retrieval (CBIR) approaches. We also discuss the strengths, limitations, and potential directions for future enhancements of the system.Keywords—SQLi, Honey Token, Zero Trust Policy, Firewall, Multi-factor Authentication.

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