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In recent years, printable graphical codes have attracted a lot of attentionenabling a link between the physical and digital worlds, which is of greatinterest for the IoT and brand protection applications. The security ofprintable codes in terms of their reproducibility by unauthorized parties orclonability is largely unexplored. In this paper, we try to investigate theclonability of printable graphical codes from a machine learning perspective.The proposed framework is based on a simple system composed of fully connectedneural network layers. The results obtained on real codes printed by severalprinters demonstrate a possibility to accurately estimate digital codes fromtheir printed counterparts in certain cases. This provides a new insight onscenarios, where printable graphical codes can be accurately cloned.

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