Counterfeiting is a pervasive issue in the collectible card market, posing significant risks to collectors, investors, and the industry at large. The trade of counterfeit cards not only undermines the value of genuine collectibles but also deceives consumers who invest their resources into fraudulent items. Despite advancements in counterfeit detection technologies, many counterfeit cards remain undiagnosed and continue to circulate in the marketplace, primarily due to the reliance on traditional methods of verification, which are often time-consuming and require expert evaluation.


This study aims to leverage the increasing capabilities of widely available consumer technologies, specifically smartphones, to develop an innovative approach for the early detection of counterfeit collectible cards. By utilizing the multi-image signals acquired from smartphone cameras, we hypothesize that significant differences in color, type font, and material reflectiveness associated with counterfeit cards can be identified through machine learning techniques, particularly convolutional neural networks (CNNs).


To evaluate this hypothesis, we analyzed a comprehensive dataset of 22,298 individual trading cards collected through the Digital Grading Company smartphone app. The dataset consisted of user-submitted images, which were systematically categorized into training, development, and test datasets to facilitate model training and validation. A robust 34-layer CNN architecture was employed to analyze these multi-image signals and predict the prevalence of counterfeit cards. The model's performance was measured using the area under the receiver operating characteristic curve (AUC), providing a quantitative assessment of its discriminatory ability.


Our results revealed that of the total cards analyzed, 6.0% were identified as counterfeit, with the CNN model achieving an AUC of 0.772 (95% CI 0.747 - 0.797) in the test dataset. This indicates a reasonable level of discrimination in detecting counterfeit cards based solely on the multi-image data. The findings suggest that deep learning technologies can significantly enhance counterfeit detection processes, providing collectors and industry stakeholders with a powerful tool to combat fraud.


This study represents the first proof-of-concept demonstration of utilizing smartphone-based imaging for the detection of counterfeits in collectible trading cards. By validating the effectiveness of deep learning in this context, we pave the way for future research that can explore more sophisticated algorithms and techniques to further improve detection accuracy. Ultimately, our approach could lead to the development of accessible applications for collectors and consumers, empowering them to make informed decisions and protect their investments in the collectible market.