Jeonggeol Kim  Jiyou Seo  Chanjae Lee  Seongmin Jo  Seungmin Kim  Seokmin Yoon  Young Yoon

10.5626/JOK.2022.49.2.137

Convolutional Neural Network auto encoder multi-modal visual data Similarity Measurement

Recently, it has become very difficult to distinguish between counterfeit products and authentic goods, and the volume of these forgeries is increasing at an alarming rate. Prompt detection of these counterfeit products is challenging since only humans can identify these forgeries through trained expertise. In this paper, given the photograph and design drawing, we use convolutional neural networks and auto-encoders to detect the possible infringement of design rights without dissembling or damaging the suspected items. We have developed an easy-to-expand system that supports the constant addition of new goods to be examined. We present the result of our system tested with a set of authentic and forged goods.