Published Paper Details:

The modern personal digital ecosystem remains under a strain of the exponentially growing multimedia content base, which leads to the unorganized media library, inefficient retrieval processes, and growing concerns about the privacy of the data and the power of the user. Classical image management systems are largely based on cloud systems in which personal media are sent to distant servers to be processed and stored, which has brought with it risks of data breach, reliance on the internet, latency, and loss of data sovereignty. The SmartMedia framework offers an Edge-AI-driven privacy-safe framework of intelligent offline media organization by fulfilling the entire media analysis pipeline on the machine of the user. The system combines a quantized Vision-Language Model that is optimized to run on the device to do automated caption, object detection, scene understanding, facial analysis and semantic tagging without depending on external APIs. The structured metadata and AI-generated descriptors are stored safely in a local SQLite database, whereas high-dimensional embeddings are indexed with FAISS to be able to provide the rapid and precise search of semantic similarity. An Electron-Python hybrid architecture will make sure there is efficient isolation between the presentation layer and computational backend which will guarantee quick user interactions as well as the management of resources. The framework will ensure that data privacy is fully achieved, the latency is minimized, and the offline functionality is not interrupted by eliminating cloud dependency and implementing secure local storage with controlled access mechanisms. The empirical testing of the prototype developed indicates that the semantic retrieval is performing high with low-computational costs on consumer-grade equipment, which confirms that it is possible to deploy advanced multimodal AI models at the edge. The suggested system defines a scalable, secure and privacy-conscious system of smart media organization in the contemporary digital contexts.