Democratizing Material Verification: A Convolutional Neural Network Approach to Sterling Silver (.925) Purity and Hallmark Integrity.

The Non-Destructive Computational Assaying (NDCA) framework addresses a critical vulnerability in the jewelry supply chain by replacing expensive, stationary hardware with an accessible, AI-driven verification system optimized for small-scale enterprises. By leveraging a fine-tuned MobileNetV2 architecture trained on a specialized \"Jeweler’s Macro-Library,\" the system analyzes microscopic surface morphologies and hallmark casting textures that are invisible to the naked eye. This approach successfully differentiates genuine .925 sterling silver from sophisticated plated counterfeits and base metals with 92.5% accuracy, while effectively flagging 98% of fraudulent hallmarks. By deploying this high-precision intelligence onto consumer-grade edge devices like smartphones, the framework democratizes material verification and provides a low-cost, scalable solution to mitigate financial risk and ensure transactional integrity across the global bullion value chain.无损计算检测（Non-Destructive Computational Assaying，NDCA）框架针对珠宝供应链中的关键安全漏洞，以专为小型企业优化的高可及性AI驱动验证系统，替代了成本高昂的固定式硬件设备。该系统基于在专属“珠宝宏观库（Jeweler’s Macro-Library）”上训练得到的微调版MobileNetV2架构，可分析肉眼不可见的微观表面形貌与印记铸造纹理。此方案能够以92.5%的准确率，有效区分.925纯银与精致镀仿制品及贱金属，同时可标记出98%的伪造贵金属印记。通过将该高精度智能系统部署至智能手机等消费级边缘设备，该框架实现了材料验证的普惠化，可为全球贵金属价值链提供低成本、可扩展的解决方案，以降低金融风险并保障全交易流程的完整性。