Pokémon Shield: A Deep learning approach for Pokémon Card Authenticator

Abstract

"In the dynamic world of Pokémon card collecting, the proliferation of counterfeit cards has emerged as a critical challenge. The market for these collectible cards, especially the coveted Generation 1 series, has become saturated with forgeries that are often indistinguishable from authentic items. The sophistication of counterfeit techniques has outpaced the development of effective identification tools, leaving a significant gap in collectors' ability to safeguard their investments. This gap is further widened by the absence of a focused approach to authenticate Generation 1 cards, alongside limitations in existing classification models that yield suboptimal accuracy. Building upon this foundation, the authentication system utilizes a deep convolutional neural network (DCNN) that leverages transfer learning to enhance its predictive accuracy. This DCNN, drawing on the pre-trained weights of renowned models like ResNet50, adapts to the unique features of Pokémon cards through additional training on a specialized dataset. The classification system employs a similar approach, using a CNN architecture tailored to recognize and categorize the vast array of Pokémon characters from the first generation. This strategy not only accelerates the training process but also provides a robust feature extraction capability, crucial for distinguishing intricate details across different card designs. The combination of transfer learning with CNN architectures ensures a potent mix of depth and precision, enabling the models to deliver high accuracy in both authenticating cards and classifying Pokémon characters. The performance of the proposed authentication system is exceptional, reflected in its precision of 0.92, recall of 0.96, and an F1-score of 0.94 for accurately identifying genuine Pokémon cards. Similarly, the classification system showcases a commendable performance with an accuracy of 0.88, a macro-average precision of 0.89, and a weighted average F1-score of 0.88, demonstrating its robust ability to categorize Generation 1 Pokémon with a high degree of precision. Future research avenues, such as integrating transfer learning and blockchain technology, aim to further augment the system’s precision and extend its utility across various collectible categories. "