Clonability of anti-counterfeiting printable graphical codes: a machine learning approach

Olga Taran; Slavi Bonev; Slava Voloshynovskiy

Clonability of anti-counterfeiting printable graphical codes: a machine learning approach

Cryptography and Security 2019-03-19 v1 Machine Learning Machine Learning

Authors: Olga Taran , Slavi Bonev , Slava Voloshynovskiy

Abstract

In recent years, printable graphical codes have attracted a lot of attention enabling a link between the physical and digital worlds, which is of great interest for the IoT and brand protection applications. The security of printable codes in terms of their reproducibility by unauthorized parties or clonability is largely unexplored. In this paper, we try to investigate the clonability of printable graphical codes from a machine learning perspective. The proposed framework is based on a simple system composed of fully connected neural network layers. The results obtained on real codes printed by several printers demonstrate a possibility to accurately estimate digital codes from their printed counterparts in certain cases. This provides a new insight on scenarios, where printable graphical codes can be accurately cloned.

Keywords

biometric authentication additive manufacturing verification

Cite

@article{arxiv.1903.07359,
  title  = {Clonability of anti-counterfeiting printable graphical codes: a machine learning approach},
  author = {Olga Taran and Slavi Bonev and Slava Voloshynovskiy},
  journal= {arXiv preprint arXiv:1903.07359},
  year   = {2019}
}

Clonability of anti-counterfeiting printable graphical codes: a machine learning approach

Abstract

Keywords

Cite

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