Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
QR codes (short for Quick Response codes) were originally developed for use in the automotive industry to track factory inventories and logistics, but their popularity has expanded significantly in the past few years due to the widespread applications of smartphones and mobile phone cameras. QR code...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/16/3/160 |
| _version_ | 1797613972472463360 |
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| author | Min-Jen Tsai Ya-Chu Lee Te-Ming Chen |
| author_facet | Min-Jen Tsai Ya-Chu Lee Te-Ming Chen |
| author_sort | Min-Jen Tsai |
| collection | DOAJ |
| description | QR codes (short for Quick Response codes) were originally developed for use in the automotive industry to track factory inventories and logistics, but their popularity has expanded significantly in the past few years due to the widespread applications of smartphones and mobile phone cameras. QR codes can be used for a variety of purposes, including tracking inventory, advertising, electronic ticketing, and mobile payments. Although they are convenient and widely used to store and share information, their accessibility also means they might be forged easily. Digital forensics can be used to recognize direct links of printed documents, including QR codes, which is important for the investigation of forged documents and the prosecution of forgers. The process involves using optical mechanisms to identify the relationship between source printers and the duplicates. Techniques regarding computer vision and machine learning, such as convolutional neural networks (CNNs), can be implemented to study and summarize statistical features in order to improve identification accuracy. This study implemented AlexNet, DenseNet201, GoogleNet, MobileNetv2, ResNet, VGG16, and other Pretrained CNN models for evaluating their abilities to predict the source printer of QR codes with a high level of accuracy. Among them, the customized CNN model demonstrated better results in identifying printed sources of grayscale and color QR codes with less computational power and training time. |
| first_indexed | 2024-03-11T07:03:04Z |
| format | Article |
| id | doaj.art-f533cb3943014411bedd1d2d5debf7a3 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-11T07:03:04Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-f533cb3943014411bedd1d2d5debf7a32023-11-17T09:09:25ZengMDPI AGAlgorithms1999-48932023-03-0116316010.3390/a16030160Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source IdentificationMin-Jen Tsai0Ya-Chu Lee1Te-Ming Chen2Institute of Information Management, National Yang Ming Chiao Tung University, 1001 Ta-Hsueh Road, Hsin-Chu 300, TaiwanInstitute of Information Management, National Yang Ming Chiao Tung University, 1001 Ta-Hsueh Road, Hsin-Chu 300, TaiwanInstitute of Information Management, National Yang Ming Chiao Tung University, 1001 Ta-Hsueh Road, Hsin-Chu 300, TaiwanQR codes (short for Quick Response codes) were originally developed for use in the automotive industry to track factory inventories and logistics, but their popularity has expanded significantly in the past few years due to the widespread applications of smartphones and mobile phone cameras. QR codes can be used for a variety of purposes, including tracking inventory, advertising, electronic ticketing, and mobile payments. Although they are convenient and widely used to store and share information, their accessibility also means they might be forged easily. Digital forensics can be used to recognize direct links of printed documents, including QR codes, which is important for the investigation of forged documents and the prosecution of forgers. The process involves using optical mechanisms to identify the relationship between source printers and the duplicates. Techniques regarding computer vision and machine learning, such as convolutional neural networks (CNNs), can be implemented to study and summarize statistical features in order to improve identification accuracy. This study implemented AlexNet, DenseNet201, GoogleNet, MobileNetv2, ResNet, VGG16, and other Pretrained CNN models for evaluating their abilities to predict the source printer of QR codes with a high level of accuracy. Among them, the customized CNN model demonstrated better results in identifying printed sources of grayscale and color QR codes with less computational power and training time.https://www.mdpi.com/1999-4893/16/3/160machine learningconvolutional neural network (CNN)quick responsedeep learningidentification of printer sourceQR Code |
| spellingShingle | Min-Jen Tsai Ya-Chu Lee Te-Ming Chen Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification Algorithms machine learning convolutional neural network (CNN) quick response deep learning identification of printer source QR Code |
| title | Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification |
| title_full | Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification |
| title_fullStr | Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification |
| title_full_unstemmed | Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification |
| title_short | Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification |
| title_sort | implementing deep convolutional neural networks for qr code based printed source identification |
| topic | machine learning convolutional neural network (CNN) quick response deep learning identification of printer source QR Code |
| url | https://www.mdpi.com/1999-4893/16/3/160 |
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