Journal of Korea Society of Industrial Information Systems (한국산업정보학회논문지)

Korea Society of Industrial Information Systems (한국산업정보학회)
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An Authentic Certification System of a Printed Color QR Code based on Convolutional Neural Network

인쇄된 컬러 QR코드의 합성곱 신경망 알고리즘에 의한 진위 판정 시스템

  • 최도영 (한밭대학교 멀티미디어공학과) ;
  • 김진수 (한밭대학교 정보통신공학과)
  • Received : 2020.03.10
  • Accepted : 2020.04.01
  • Published : 2020.06.30
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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.

스마트폰의 대중적인 보급으로 인해 QR 코드는 세상에서 가장 보편적인 코드들 중의 하나가 되었다. 본 논문에서는 새로운 형태의 QR 코드를 제안하여 저장 용량을 증가시키고, 또한, 컬러정보와 패턴 형태를 가변시켜서 개인 정보를 포함할 수 있게 한다. 이와 더불어, 제안된 QR 코드가 인쇄된 형태의 다양한 응용환경에 작용될 수 있도록 본 논문은 효과적인 진위 판정 시스템을 제안한다. 제안한 시스템은 기존의 합성곱 신경망 구조 즉 VGGNet으로 구현되며, 스마트 폰을 통해 손쉽게 진품 또는 가품을 판정하고, 진품으로 판정된 코드에 대해서는 삽입된 개인 정보를 추출하도록 설계된다. 인쇄된 QR 코드에 대한 실제의 다양한 실험을 통해 제안된 시스템은 진품 또는 가품을 거의 완벽하게 분류할 수 있음을 보이고 개인 정보를 효과적으로 추출할 수 있음을 확인한다.

Keywords

References

  1. Andre, P., and Ferreria, R. (2014). Colour Multiplexing of Quick-Response (QR) Codes, Electronics Letters (IET), 50(24), 1828-1830. https://doi.org/10.1049/el.2014.2501
  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. https://doi.org/10.9723/JKSIIS.2018.23.4.001
  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. https://doi.org/10.5392/JKCA.2018.18.10.431
  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. https://doi.org/10.5392/JKCA.2011.11.5.125
  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. 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. https://doi.org/10.9723/jksiis.2016.21.2.021
  9. 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. https://doi.org/10.9723/jksiis.2017.22.2.027
  10. Simonyan, K., and Zisseman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition, ICLR 2015, 1-14.
  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. https://doi.org/10.6109/jkiice.2016.20.1.142
  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.

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