Browse > Article
http://dx.doi.org/10.9723/jksiis.2020.25.3.021

An Authentic Certification System of a Printed Color QR Code based on Convolutional Neural Network  

Choi, Do-young (한밭대학교 멀티미디어공학과)
Kim, Jin-soo (한밭대학교 정보통신공학과)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.25, no.3, 2020 , pp. 21-30 More about this Journal
Abstract
With the widespread of smartphones, the Quick response (QR) code became one of the most popular codes. In this paper, a new type of QR code is proposed to increase the storage capacities and also to contain private information by changing the colors and the shape of patterns in the codes. Then, for a variety of applications of the printed QR codes, this paper proposes an efficient authentic certification system, which is built on an conventional CNN (Convolutional neural network) architecture - VGGNet and classifies authentic or counterfeit with smartphones, easily. For authentic codes, the proposed system extracts the embedded private information. Through practical experiments with a printed QR code, it is shown that the proposed system can classify authentic or counterfeit code, perfectly, and also, are useful for extracting private information.
Keywords
Authentic certification; Color QR code; Scan&printing;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
연도 인용수 순위
1 Andre, P., and Ferreria, R. (2014). Colour Multiplexing of Quick-Response (QR) Codes, Electronics Letters (IET), 50(24), 1828-1830.   DOI
2 Choi, D., and Kim, J. (2018a). A Code Authentication System of Counterfeit Printed Image Using Multiple Comparison Measures, Journal of the Korea Industrial Information Systems Research, 23(4), 1- 12.   DOI
3 Choi, D., and Kim, J. (2018b). An Effective Detection of Print Image Forgeries based on Modeling of Color Matrix: An Application to QR Code, The Journal of the Korea Contents Association, 18(10), 431-442.   DOI
4 Galiyawala, H., and Pandya, K. (2014). To Increase Data Capacity of QR Code using Multiplexing with Color Coding, 2014 Annual IEEE India Conference (INDICON), 1-6.
5 Jung, J., Yang, H., Kim, S., Lee, G., and Kim, S. (2011). Wine Label Recognition System using Image Similarity, The Journal of the Korea Contents Association, 11(5), 125-137.   DOI
6 Kim, J. (2019). Recognition Performance Improvement of QR and Color Codes Posted on Curved Surfaces, Journal of the Korea Institute of Information and Communication Engineering (JKI ICE), 267-275.
7 Nandhini, S. (2017). Performance Evaluation of Embedded Color or Codes on Logos, Third International Conference On Science Technology Engineering and Management (ICONSTEM), 1009-1014.
8 Simonyan, K., and Zisseman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition, ICLR 2015, 1-14.
9 Ryu, J., and Kim, J. (2016). Performance Comparison of BCS-SPL Techniques against a Variety of Restoring Block Sizes, Journal of the Korea Industrial Information Systems Research, 21(2), 21- 28.   DOI
10 Ryu, J., and Kim, J. (2017). Reconstructed Image Quality Improvement of Distributed Compressive Video Sensing using Temporal Correlation, Journal of the Korea Industrial Information Systems Research, 22(2), 27- 34.   DOI
11 Song, J., and Lee, J. (2016). Positioning Method using a Vehicular Black-Box Camera and a 2D Barcode in an Indoor Parking Lot, Journal of the Korea Institute of Information and Communication Engineering, 20(1), 142-152.   DOI
12 Thompson, N., and Lee, K. (2013). Information Security Challenge of QR Codes, Journal of Digital Forensics Security and Law, 8(2), 43-72.
13 Tkachenko, I., Puech, W., Strauss, O., Gaudin, J., Destruel, C., and Guichard, C. (2015). Rich QR Code for Multimedia Management Applications, Image Analysis and Processing (ICIAP ), 383-393.
14 Tkachenko, I., Puech, W., Strauss, O., Destruel, C., and Gaudin, J. (2016). Printed Document Authentication using Two Level QR Code, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2149-2153.