  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.
