Antique Hub: Deep Learning based Verification System and Marketplace for Antique Collectibles

In the digital transformation era, the antique market is undergoing a significant transition, marked by the emergence of ‘Antique Hub’. This e-commerce platform exclusively caters to antique enthusiasts, providing a distinctive avenue to explore and acquire genuine items. Distinguished by its innovative integration of deep learning technology, specifically Convolutional Neural Networks (CNNs) such as Inception V3 and VGGNet, the platform achieves notable accuracies of 96% in Inception V3 and 94% in VGG16. The utilization of advanced deep learning techniques, notably CNN models, plays a pivotal role in the authentication process, ensuring the exclusive showcasing of authentic antique products. Rigorous evaluation validates the precision of these models in identifying and authenticating items. In addition to authentication, a comprehensive analysis of sales data is undertaken, providing insights into platform performance and its efficacy in connecting sellers with buyers. This analysis enables a nuanced understanding of market trends, user preferences, and overall platform success, contributing to continuous improvement of the user experience. This study elucidates the purpose and methodology of ‘Antique Hub’, highlighting the cutting-edge CNN model’s role in authentication and presenting significant findings, including impressive accuracies. The implications underscore the platform’s commitment to trust, authenticity, and the ongoing enhancement of the user experience, ultimately reshaping the antique market in the digital era.