Title:	ENHANCEMENT OF AI-BASED IMPLEMENTATION USING A ONE-STAGE DETECTOR ALGORITHM FOR THE DETECTION OF COUNTERFEIT PRODUCTS
Author(s):	Eduard Daoud, Nabil Khalil and Martin Gaedke
ISBN:	978-989-8704-38-2
Editors:	Piet Kommers, Inmaculada Arnedillo Sánchez and Pedro Isaías
Year:	2022
Edition:	Single
Keywords:	Anti-Counterfeiting, Machine Learning, Deep Learning, Image Recognition, Object Detection
First Page:	107
Last Page:	114
Language:	English
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Paper Abstract:	Counterfeit products are a major problem that the market has been facing for a long time. According to the Global Brand Counterfeiting Report 2018 "Amount of Total Counterfeiting, globally has reached to 1.2 Trillion USD in 2017 and is Bound to Reach 1.82 Trillion USD by the Year 2020", a solution to this concern has already been researched and published by the authors in previous research papers published in e-society 2020 and IADIS journal, but the issue with the previously mentioned solution was that the object detection performance and accuracy needed to be improved. In this paper, a comparison between the current YOLO (You Only Look Once) algorithm used in the new implementation and the SSD (Single Shot Detector) algorithm, the faster R-CNN (Region-Based Convolutional Neural Networks) used in the old implementation, is made in the context of the present task to discuss and prove why YOLO is a more suitable option for the counterfeit product detection task.
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