Hybrid Anomaly Detection for Bullion Coin Authentication Leveraging Acoustic Signature Analysis
For precious metals market stakeholders, this provides a non-destructive method to combat sophisticated counterfeits, but the results are incremental and lack concrete performance numbers.
The paper proposes a non-destructive acoustic-based framework using an autoencoder and deep learning classifier to authenticate bullion coins, achieving high precision in distinguishing authentic coins from counterfeits across varying conditions.
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.