|
Title:
|
IMPROVING FAKE PRODUCT DETECTION USING AI-BASED TECHNOLOGY |
|
Author(s):
|
Eduard Daoud, Dang Vu, Hung Nguyen and Martin Gaedke |
|
ISBN:
|
978-989-8704-14-6 |
|
Editors:
|
Piet Kommers, Boyan Bontchev and Pedro Isaías |
|
Year:
|
2020 |
|
Edition:
|
Single |
|
Keywords:
|
Anti-Counterfeiting, Machine Learning, Deep Learning, Image Recognition, Object Detection |
|
Type:
|
Full |
|
First Page:
|
119 |
|
Last Page:
|
125 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
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 (RESEARCH AND
MARKETS, 2018). This paper does not consider copyrights or digital piracy, counterfeiting, fraudulent documents but
rather investigates the prevention of counterfeiting on a technological basis. The presence of counterfeit products on the
European market is on the increase, therefore the intervention of inspection bodies and authorities alone is not sufficient,
consumers can make their contribution and support this process. 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 be a key technology in the fight against counterfeiting. The automatic image and
text recognition and the classification of product information enable end customers to detect counterfeits precisely and
quickly by checking them against trained models. The goal of this paper is to create an easy to use applications in which
the end-user identifies the counterfeit product and contribute to the fight against product piracy. |
|
|
|
|
|
|
