TY  - JOUR
T1  - Detecting Design Infringement Using Multi-Modal Visual Data and Auto Encoder based on Convolutional Neural Network
AU  - Kim, Jeonggeol 
AU  - Seo, Jiyou 
AU  - Lee, Chanjae 
AU  - Jo, Seongmin 
AU  - Kim, Seungmin 
AU  - Yoon, Seokmin 
AU  - Yoon, Young 
JO  - Journal of KIISE, JOK
PY  - 2022
DA  - 2022/1/14
DO  - 10.5626/JOK.2022.49.2.137
KW  - convolutional neural network
KW  - auto encoder
KW  - multi-modal visual data
KW  - similarity measurement
AB  - 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.