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3DPS: 3D Printing Signature for Authentication Based on Equipment Distortion Model

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Digital Forensics and Watermarking (IWDW 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14511))

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Abstract

To counter the counterfeit of 3D printed products, 3DPS, an improved printing signature for 3D printed objects based on our previous work is proposed for authentication. A specific hole is added to the non-critical flat portion of the 3D printed object, and the 3DPS is constructed from the contour of the hole with a hand-held microscope. Compared to the previous work, the equipment distortion model and the 3DPS’s construction are improved, and the threshold can be calculated directly rather than determined by experience. Experimental results show that the 3DPS not only can effectively authenticate 3D printed objects with high accuracy but is also robust and secure.

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Acknowledgement

This work was supported in part by projects supported by Natural Science Foundation of Guangdong Province (Grant No. 2023A1515011575), Science and Technology Program of Guangzhou under Grant No. 2024A03J0092, National Natural Science Foundation of China (Grant No. 62072055, U1936115, 92067104).

Author information

Authors and Affiliations

  1. Institute of Artificial Intelligence, Guangzhou University, Guangzhou, 510006, Guangdong, China

    Fei Peng

  2. School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, 510006, Guangdong, China

    Min Long

Authors
  1. Fei Peng
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  2. Min Long
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Corresponding author

Correspondence to Fei Peng .

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Editors and Affiliations

  1. School of Cyber Security, Qilu University of Technology, Jinan, China

    Bin Ma

  2. Qilu University of Technology, Jinan, China

    Jian Li

  3. Qilu University of Technology, Jinan, China

    Qi Li

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Peng, F., Long, M. (2024). 3DPS: 3D Printing Signature for Authentication Based on Equipment Distortion Model. In: Ma, B., Li, J., Li, Q. (eds) Digital Forensics and Watermarking. IWDW 2023. Lecture Notes in Computer Science, vol 14511. Springer, Singapore. https://doi.org/10.1007/978-981-97-2585-4_10

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  • DOI: https://doi.org/10.1007/978-981-97-2585-4_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2584-7

  • Online ISBN: 978-981-97-2585-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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