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