The verification of bullion coin authenticity is essential for maintaining integrity within the precious metals market; however, the increasing sophistication of counterfeits has rendered traditional inspection methods insufficient. This paper proposes a non-destructive verification framework based on acoustic frequency analysis and deep neural networks. The methodology leverages the unique acoustic fingerprint of a coin, a physical signature determined by its material composition, mass, and geometry, captured through mechanical excitation. We implement a synergistic dual-model architecture consisting of an autoencoder that reconstructs the spectrum for anomaly detection and a deep learning classifier for coin type identification. To address the challenges of environmental noise and limited dataset diversity, a dynamically calculated anomaly threshold and data augmentation techniques were employed. Experimental results demonstrate that the integrated system achieves high precision in distinguishing authentic specimens from high-quality counterfeits, maintaining stability across varying recording conditions and devices. Beyond bullion authentication, the study highlights the scalability of the proposed non-destructive testing method for assessing the safety of critical components in the automotive and aerospace industries.

翻译：金条硬币真伪验证对于维护贵金属市场的完整性至关重要，然而伪造技术的日益精进使传统检测方法难以胜任。本文提出一种基于声学频率分析与深度神经网络的无损验证框架。该方法通过机械激励捕获硬币的独特声学指纹——这一由材料成分、质量与几何结构共同决定的物理特征。我们构建了一种协同双模型架构，包含用于频谱重构及异常检测的自编码器，以及用于硬币类型识别的深度学习分类器。为应对环境噪声与有限数据集多样性的挑战，采用了动态计算的异常阈值与数据增强技术。实验结果表明，该集成系统在区分真品与高质量伪造品方面实现了高精度，且在跨录音条件与设备环境下保持稳定性。除贵金属认证外，本研究还突显了所提无损检测方法在汽车与航空航天领域关键部件安全性评估中的可扩展性。