Hybrid Anomaly Detection for Bullion Coin Authentication Leveraging Acoustic Signature Analysis

TL;DR

This paper introduces a non-destructive acoustic analysis method using deep neural networks to authenticate bullion coins, effectively detecting counterfeits despite environmental noise and limited data.

Contribution

It presents a novel dual-model deep learning framework combining autoencoders and classifiers for coin authentication based on acoustic signatures.

Findings

High precision in distinguishing authentic coins from counterfeits

Stable performance across different recording conditions and devices

Effective data augmentation and adaptive thresholds enhance detection accuracy

Abstract

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.