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Dual-Layer Barcodes

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Pattern Recognition and Computer Vision (PRCV 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13020))

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Abstract

With the emergence of mobile networks and smartphones, Quick Response Code (QR Code) has been widely used in many scenes in life, e.g. mobile payments, advertisement, and product traceability. However, when the encoded information of QR Codes is leaked, the QR Codes can be easily copied, which increases the risk of mobile payment and the difficulty of product traceability. To solve these problems, this paper proposes a novel approach to expand the information channels of QR Code based on invisible data hiding. The proposed architecture consists of an information encoder to hide messages into QR Codes while maintaining the original appearances of the QR codes and an information decoder to extract the hidden messages. To make the hidden messages detectable by smartphones, we use a series of noise layers to process the encoder output between the end-to-end training of the encoder and the decoder. The noise layers simulate the general distortions caused by camera imaging, e.g. noise, blur, JPEG compression, and light reflection. Experimental results show that the proposed method can achieve a high decoding accuracy of the hidden messages without affecting the decoding rate of the QR Codes used as the containers.

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Author information

Authors and Affiliations

  1. The Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shang-hai, 200240, China

    Kang Fu, Jun Jia & Guangtao Zhai

Authors
  1. Kang Fu
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  2. Jun Jia
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  3. Guangtao Zhai
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Corresponding author

Correspondence to Guangtao Zhai .

Editor information

Editors and Affiliations

  1. University of Science and Technology Beijing, Beijing, China

    Huimin Ma

  2. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Tsinghua University, Beijing, China

    Changshui Zhang

  4. Zhejiang University, Hangzhou, China

    Fei Wu

  5. Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hunan University, Changsha, China

    Yaonan Wang

  7. Sun Yat-Sen University, Guangzhou, Guangdong, China

    Jianhuang Lai

  8. Beijing Jiaotong University, Beijing, China

    Yao Zhao

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Fu, K., Jia, J., Zhai, G. (2021). Dual-Layer Barcodes. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13020. Springer, Cham. https://doi.org/10.1007/978-3-030-88007-1_18

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  • DOI: https://doi.org/10.1007/978-3-030-88007-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88006-4

  • Online ISBN: 978-3-030-88007-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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