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Distributed and Self-organizing Systems

PUBLICATION

Enhancing Fake Product Detection Using Deep Learning Object Detection Models

Type

Journal Article

Year

2020

Authors

daoud

Dang Vu Nguyen Hai

Dang Vu Nguyen Hai

Hung Nguyen

Hung Nguyen

gaedke

Research Area

Intelligent Information Management

Event

IADIS International Journal on Computer Science and Information Systems

ISBN/ISSN

1646-3692

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Abstract

ENHANCING FAKE PRODUCT DETECTION USING DEEP LEARNING OBJECT DETECTION MODELS

ResearchAndMarkets wrote in their report on May 15, 2018, that up to 1.2 Trillion USD in 2017 of products are counterfeited goods. The report estimated this damage globally at 1.82 Trillion USD in 2020. This paper does not consider copyright infringement, digital piracy, counterfeiting or fraudulent documents, but rather examines the prevention of counterfeiting on a technological basis. The presence of counterfeit products on the European and US markets increase, the intervention of inspection bodies and authorities alone is obviously not sufficient, but consumers could make their contribution and improve the situation. In this paper, we research the possibility to reduce counterfeit products using machine learning-based technology. Image and text recognition, and classification based on machine learning have the potential to become the key technology in the fight against counterfeiting. Image recognition and classification of product information empower the end customer to identify counterfeits accurately and efficiently by comparing them with trained models. The goal of this paper is to create an easy, simple, and elegant application, which empowers the end-users to identify counterfeit products and as such contribute to the fight against product piracy.

 

Reference

Daoud, Eduard; Hai, Dang V. N.; Nguyen, Hung; Gaedke, Martin: Enhancing Fake Product Detection Using Deep Learning Object Detection Models, pp. 13-24, 2020.



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