Abstract:
Within the e-commerce industry, online counterfeiting continues to be a
significant concern. Advanced solutions are required due to the sophistication and
adaptability of counterfeiting techniques. The Organization for Economic Co
operation and Development (OECD) has noted that in 2019, counterfeiting
accounted for USD 464 billion, or 2.5 percent, of all commerce worldwide. The
wider social ramifications, like child labor, drug trafficking, and money
laundering, highlight how urgent intervention is. In response, our study began
analyzing 23,000 Paris Saint-Germain (PSG)-related e-commerce listings from
thirty well-known platforms, such as Redbubble, Alibaba, Amazon, and Mercado
Libre. A composite classifier was created by combining textual (Title,
Description, Seller Name, and Product URL) and image data. By utilizing the
Self-Organized Feature Map together with a surrogate model, this multi-modal
method was able to detect real listings from fake listings with an astounding 90%
accuracy rate. By combining text and image analytics, this all-encompassing
approach provides a strong and all-encompassing anti-counterfeiting strategy that
strengthens the integrity of e-commerce platforms and guarantees a safer online
marketplace for users.
