Predicting Counterfeits from Smartphone Multi-Images Using Deep Learning

Abstract

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 ofverification, 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  sophisticatedalgorithms  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