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Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block

With the rapid development of information technology and the widespread use of the Internet, QR codes are widely used in all walks of life and have a profound impact on people’s work and life. However, the QR code itself is likely to be printed and forged, which will cause serious economic losses an...

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Detalles Bibliográficos
Autores principales: Guo, Zhongyuan, Zheng, Hong, You, Changhui, Xu, Xiaohang, Wu, Xiongbin, Zheng, Zhaohui, Ju, Jianping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7663918/
https://www.ncbi.nlm.nih.gov/pubmed/33167526
http://dx.doi.org/10.3390/s20216305
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author Guo, Zhongyuan
Zheng, Hong
You, Changhui
Xu, Xiaohang
Wu, Xiongbin
Zheng, Zhaohui
Ju, Jianping
author_facet Guo, Zhongyuan
Zheng, Hong
You, Changhui
Xu, Xiaohang
Wu, Xiongbin
Zheng, Zhaohui
Ju, Jianping
author_sort Guo, Zhongyuan
collection PubMed
description With the rapid development of information technology and the widespread use of the Internet, QR codes are widely used in all walks of life and have a profound impact on people’s work and life. However, the QR code itself is likely to be printed and forged, which will cause serious economic losses and criminal offenses. Therefore, it is of great significance to identify the printer source of QR code. A method of printer source identification for scanned QR Code image blocks based on convolutional neural network (PSINet) is proposed, which innovatively introduces a bottleneck residual block (BRB). We give a detailed theoretical discussion and experimental analysis of PSINet in terms of network input, the first convolution layer design based on residual structure, and the overall architecture of the proposed convolution neural network (CNN). Experimental results show that the proposed PSINet in this paper can obtain extremely excellent printer source identification performance, the accuracy of printer source identification of QR code on eight printers can reach 99.82%, which is not only better than LeNet and AlexNet widely used in the field of digital image forensics, but also exceeds state-of-the-art deep learning methods in the field of printer source identification.
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spelling pubmed-76639182020-11-14 Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block Guo, Zhongyuan Zheng, Hong You, Changhui Xu, Xiaohang Wu, Xiongbin Zheng, Zhaohui Ju, Jianping Sensors (Basel) Article With the rapid development of information technology and the widespread use of the Internet, QR codes are widely used in all walks of life and have a profound impact on people’s work and life. However, the QR code itself is likely to be printed and forged, which will cause serious economic losses and criminal offenses. Therefore, it is of great significance to identify the printer source of QR code. A method of printer source identification for scanned QR Code image blocks based on convolutional neural network (PSINet) is proposed, which innovatively introduces a bottleneck residual block (BRB). We give a detailed theoretical discussion and experimental analysis of PSINet in terms of network input, the first convolution layer design based on residual structure, and the overall architecture of the proposed convolution neural network (CNN). Experimental results show that the proposed PSINet in this paper can obtain extremely excellent printer source identification performance, the accuracy of printer source identification of QR code on eight printers can reach 99.82%, which is not only better than LeNet and AlexNet widely used in the field of digital image forensics, but also exceeds state-of-the-art deep learning methods in the field of printer source identification. MDPI 2020-11-05 /pmc/articles/PMC7663918/ /pubmed/33167526 http://dx.doi.org/10.3390/s20216305 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Guo, Zhongyuan
Zheng, Hong
You, Changhui
Xu, Xiaohang
Wu, Xiongbin
Zheng, Zhaohui
Ju, Jianping
Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block
title Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block
title_full Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block
title_fullStr Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block
title_full_unstemmed Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block
title_short Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block
title_sort digital forensics of scanned qr code images for printer source identification using bottleneck residual block
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7663918/
https://www.ncbi.nlm.nih.gov/pubmed/33167526
http://dx.doi.org/10.3390/s20216305
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