Image Classifier for an Online Footwear Marketplace to Distinguish between Counterfeit and Real Sneakers for Resale

Onalaja, Joshua and Shahra, Essa and Basurra, Shadi and Jabbar, Waheb A. (2024) Image Classifier for an Online Footwear Marketplace to Distinguish between Counterfeit and Real Sneakers for Resale. Sensors, 24 (10). p. 3030. ISSN 1424-8220

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The sneaker industry is continuing to expand at a fast rate and will be worth over USD 120 billion in the next few years. This is, in part due to social media and online retailers building hype around releases of limited-edition sneakers, which are usually collaborations between well-known global icons and footwear companies. These limited-edition sneakers are typically released in low quantities using an online raffle system, meaning only a few people can get their hands on them. As expected, this causes their value to skyrocket and has created an extremely lucrative resale market for sneakers. This has given rise to numerous counterfeit sneakers flooding the resale market, resulting in online platforms having to hand-verify a sneaker’s authenticity, which is an important but time-consuming procedure that slows the selling and buying process. To speed up the authentication process, Support Vector Machines and a convolutional neural network were used to classify images of fake and real sneakers and then their accuracies were compared to see which performed better. The results showed that the CNNs performed much better at this task than the SVMs with some accuracies over 95%. Therefore, a CNN is well equipped to be a sneaker authenticator and will be of great benefit to the reselling industry.

Item Type: Article
Identification Number:
9 May 2024Accepted
10 May 2024Published Online
Uncontrolled Keywords: sneakers, SVM, Alex architecture, VGGI architecture, LeNet-5, bespoke architecture
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Gemma Tonks
Date Deposited: 05 Jun 2024 14:42
Last Modified: 05 Jun 2024 14:42

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