Abstract:
Online counterfeiting has become a significant threat to the e-commerce industry
recently. It is becoming difficult to take countermeasures as the methods, tactics, and
approaches of counterfeiting are evolving, and it is difficult to create a one-stop
solution. According to the Organization for Economic Co-operation and Development
(OECD), counterfeiting accounted for USD 464 billion or 2.5% of world trade in
2019. Counterfeiting generally contributes to factors such as child labour, illegal drug
trafficking, and money laundering, which highlights this as a significant area for
further study. This study uses 23000 e-commerce listings related to Paris Saint
Germain (PSG) in thirty (30) e-commerce marketplaces such as Alibaba, Amazon,
Redbubble, and Mercado Libre to train a text classifier based on title, description,
seller name, and product URL. This study uses Random Forest Classifier and presents
results with 95% accuracy. Also, this study focused on the provisions of an image
classifier to make better decisions in anti-counterfeiting strategies in e-commerce.
