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

arXiv: https://arxiv.org/abs/1903.07359 · HTML: https://mlsanity.com/paper/1903.07359/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2019.

## Built on by (most cited first, 7 of 7)

- [Machine learning attack on copy detection patterns: are 1x1 patterns cloneable?](https://mlsanity.com/paper/2110.02176/) (2021)
- [Mathematical model of printing-imaging channel for blind detection of fake copy detection patterns](https://mlsanity.com/paper/2212.07326/) (2022)
- [Mobile authentication of copy detection patterns](https://mlsanity.com/paper/2203.02397/) (2022)
- [Printing variability of copy detection patterns](https://mlsanity.com/paper/2210.05343/) (2022)
- [Authentication of Copy Detection Patterns under Machine Learning Attacks: A Supervised Approach](https://mlsanity.com/paper/2206.11793/) (2022)
- [Provable Performance Guarantees of Copy Detection Patterns](https://mlsanity.com/paper/2409.17649/) (2024)
- [Diffusion-Based Authentication of Copy Detection Patterns: A Multimodal Framework with Printer Signature Conditioning](https://mlsanity.com/paper/2603.08998/) (2026)

## Abstract (arXiv)

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.