Evidence map›Paper›PMID 38793887›Full record

ArticleSensors (Basel, Switzerland)2024

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

Joshua Onalaja, Essa Q Shahra, Shadi Basurra, Waheb A Jabbar

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Joshua OnalajaFaculty of Computing, Engineering and Built Environment, Birmingham City University, Birmingham B4 7RQ, UK.
Essa Q ShahraFaculty of Computing, Engineering and Built Environment, Birmingham City University, Birmingham B4 7RQ, UK.ORCID 0000-0002-3668-6230
Shadi BasurraFaculty of Computing, Engineering and Built Environment, Birmingham City University, Birmingham B4 7RQ, UK.
Waheb A JabbarFaculty of Computing, Engineering and Built Environment, Birmingham City University, Birmingham B4 7RQ, UK.ORCID 0000-0001-5164-8403

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Alex architecturebespoke architectureLeNet-5sneakersSVMVGGI architecture

Identifiers

PMID38793887
PMCPMC11124814

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.