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Open-Set Product Authentication Based on Deep Texture Verification

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Image and Graphics (ICIG 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12888))

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Abstract

The authenticity of consumer products has a significant impact on the economic and social issues of countries around the world. Due to the recent advancements in machine learning, there emerges some authentication techniques, in a close-set setting, based on texture features extracted from the product surfaces. However, the existing techniques have suffered from the problem of low accuracies or inability to deal with unknown classes in open-set authentication. In this work, we build an anti-counterfeiting system that works for consumer products in an open-set scenario. Different from other anti-counterfeiting methods, this work considers the problem of product authentication as a simple texture verification process. It allows the authentication being conducted under both close-set and open-set scenarios by a distance comparison operation with some customized metrics in the embedding space. We evaluate our system with two state-of-the-art texture databases. Experimental results show that the proposed system achieves 89.91% open-set authentication accuracy for a feature of 256 dimensions in the Outex texture database.

This work was supported in part by Guangdong Basic and Applied Basic Research Foundation (Grant 2020A1515010563, 2019B151502001), National Science Foundation of China (Grant 62072313), and Shenzhen R&D Program (Grant JCYJ20180305124550725, 20200813110043002, JCYJ20200109105008228).

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Author information

Authors and Affiliations

  1. The Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen, 518060, China

    Sudao Cai, Lin Zhao & Changsheng Chen

  2. The Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518129, China

    Sudao Cai, Lin Zhao & Changsheng Chen

Authors
  1. Sudao Cai
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  2. Lin Zhao
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  3. Changsheng Chen
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Corresponding author

Correspondence to Changsheng Chen .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

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Cai, S., Zhao, L., Chen, C. (2021). Open-Set Product Authentication Based on Deep Texture Verification. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12888. Springer, Cham. https://doi.org/10.1007/978-3-030-87355-4_10

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  • DOI: https://doi.org/10.1007/978-3-030-87355-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87354-7

  • Online ISBN: 978-3-030-87355-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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