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

## TL;DR

This paper investigates the clonability of printable graphical codes using machine learning, revealing that neural networks can accurately reproduce digital codes from printed versions, raising security concerns for IoT and brand protection.

## Contribution

It introduces a neural network-based framework to assess the clonability of printable graphical codes, providing new insights into their security vulnerabilities.

## Key findings

- Neural networks can accurately estimate digital codes from printed ones.
- Clonability varies depending on printer type and code design.
- Potential security risks for IoT and brand protection applications.

## 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.