The quality of counterfeit items has increased dramatically, with modern global manufacturing being able to duplicate the materials, construction, and visual features of items. Detection of fraudulent coinage can parallel authentication of food, beverages, and manufactured goods by studying product-inherent features. Counterfeit detection is performed by comparing an Example group with a Questioned group. A model is developed for both groups using standard tests on individual pieces. Coin weight is used here as an illustration. The model should also follow the natural science of the system. In this case, the manufacturing process variation is known and steady, and the underlying distribution is known or can be determined from authentic pieces. The proposed detection method uses testing of many individual pieces, then using reverse-quality-engineering methods to identify possible sources. This strategy looks at the variation between individual pieces to determine the process capability of a machine, assembly line, or plant to create product consistency for a manufacturer. Fraudulent items may be manufactured within specification, but demonstrate a manufacturing process capability different than that of the authentic manufacturer. In this report, we examine the model previously reported and use reconstruction techniques to re-create the evidence set to validate the model, increase model accuracy, and confirm the conclusion previously reached, showing that the Questioned set is likely over 37% non-conforming by weight. In this case, the decision outcome of the analysis was improved by using additional methods not included in the modeling software package originally used.
Keywords: Gaussian finite mixture model; Monte Carlo simulation; coin authentication; counterfeit detection; finite mixture model; population comparisons; product-inherent features.
Published 2023. This article is a U.S. Government work and is in the public domain in the USA.