A Deep Learning Approach for QR Code Based Printed Source Identification

Min-Jen Tsai, Te Ming Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2021 15th International Conference on Signal Processing and Communication Systems, ICSPCS 2021 - Proceedings
EditorsTadeusz A Wysocki, Beata J Wysocki
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665436991
DOIs
StatePublished - 2021
Event15th International Conference on Signal Processing and Communication Systems, ICSPCS 2021 - Virtual, Online, Australia
Duration: 13 Dec 202115 Dec 2021

Publication series

Name2021 15th International Conference on Signal Processing and Communication Systems, ICSPCS 2021 - Proceedings

Conference

Conference15th International Conference on Signal Processing and Communication Systems, ICSPCS 2021
Country/TerritoryAustralia
CityVirtual, Online
Period13/12/2115/12/21

Keywords

  • Convolutional Neural Network (CNN)
  • Deep Learning
  • Machine Learning
  • printer source identification
  • QR code
  • quick response

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