Clonability of anti-counterfeiting printable graphical codes: a machine learning approach
This addresses security vulnerabilities in anti-counterfeiting for IoT and brand protection, but is incremental as it applies an existing method to a new problem.
The paper tackled the problem of clonability of printable graphical codes by using a fully connected neural network to estimate digital codes from printed versions, achieving accurate cloning in certain cases.
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.