@inproceedings{662b383bceef4d97b936e36fd0998e36,
title = "A Deep Learning Approach for QR Code Based Printed Source Identification",
abstract = "With the widespread popularity and its ease of use, QR codes are relatively easy to be reproduced or forged illegally through printed documents. One solution to tackle this problem is to identify the source printer which produced the QR codes. In this paper, we study the CNN models for QR code based printed source identification through a series of experiments which involved with grayscale QR codes. Our experimental results show that the pretrained CNN models such as AlexNet, GoogleNet and ResNet could identify printer source with high accuracy even though the models are trained with limited input dataset. ",
keywords = "Convolutional Neural Network (CNN), Deep Learning, Machine Learning, printer source identification, QR code, quick response",
author = "Min-Jen Tsai and Chen, {Te Ming}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 15th International Conference on Signal Processing and Communication Systems, ICSPCS 2021 ; Conference date: 13-12-2021 Through 15-12-2021",
year = "2021",
doi = "10.1109/ICSPCS53099.2021.9660343",
language = "English",
series = "2021 15th International Conference on Signal Processing and Communication Systems, ICSPCS 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Wysocki, {Tadeusz A} and Wysocki, {Beata J}",
booktitle = "2021 15th International Conference on Signal Processing and Communication Systems, ICSPCS 2021 - Proceedings",
address = "United States",
}