The role of AI in detecting and preventing counterfeit trading cards

Trading card counterfeiting is the production and distribution of fake or imitation trading cards. These cards are often made to look identical to genuine cards and this makes its difficult to distinguish from the real thing. In this post we will cover how AI can impact this area of the industry and provide positive benefits.

Check out this recent article written by Doug Reardon where a man was recently caught counterfeiting over $7M worth of trading cards. Click here

AI can be used in a few different ways to identify counterfeit cards. Image recognition is the most common and this is where patterns are recognized in the trading cards. It could be anything from the font, card thickness, or holographic patterns from printing that can help to verify an authentic card. Machine learning (ML) algorithms can be taught on a dataset of genuine trading cards to visualize characteristics. When a new card is analyzed, the ML will compare that card to the genuine card dataset and flag counterfeit cards.

AI does not even have to only be reviewing a trading card to help identify counterfeiting schemes. Analyzing the trading card market to detect unusual spikes in sales, identify sellers with anomaly cards for sale, or an unusual number of cards that have entered the market are a great way AI can be used in counterfeit pattern detection. AI could alert a buyer, or potentially authorities, of cards that goes up for sale that could be counterfeit. Population reports, exchange listings, and card data sets can be constantly analyzed by AI who could identify if there were 10 cards printed and an 11th card magically enters the market.

Blockchain technology is a popular advancement that we foresee being used to create a secure and traceable supply chain for trading cards. A tamper-proof record, stored on the blockchain once a card is printed would reduce counterfeiting. For current cards in circulation, there will need to be an approved authentication process to apply the same technology.

We should quickly hit on the limitations of using AI in detecting and preventing counterfeit trading cards. Counterfeiters continually find new ways to produce high-quality copies of cards, making it very difficult to continually train the AI and ML algorithm. The effectiveness of these algorithms are also highly dependent on the quality and quantity of data they are trained on. If the data is incomplete or not representative of the entire population of cards, the algorithm may not be able to detect all counterfeit cards. Lastly, implementing AI and ML technology can be costly, and the cost of these technologies may be prohibitive for small business and individual collectors.

Check out some of these companies already using AI to improve their ability to authenticate:

• PSA

• Beckett

• Collectors Universe

• Collectable

• eBay who is using machine learning to detect and remove counterfeit trading cards on its platform.

There are multiple use cases for AI and ML to prevent fraudulent cards from negatively impacting #TheHobby. From pattern recognition to genuine trading card datasets, the future of technology enabling the industry to reduce counterfeiting is bright. Flygrade plans to be using its application to assist with counterfeit detection in the future, but for now we are solely focused on grading trading cards.

AI is not a magical solution and won't be the final step in counterfeit prevention. But, it is a great resource that can make it more difficult for counterfeiters to operate and make it easier for buyers to detect fake cards.

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