# Assessing the Viability of Synthetic Physical Copy Detection Patterns on Different Imaging Systems

arXiv: https://arxiv.org/abs/2410.02575 · HTML: https://mlsanity.com/paper/2410.02575/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2024.

## Built on by (most cited first, 3 of 3)

- [Diffusion-Based Authentication of Copy Detection Patterns: A Multimodal Framework with Printer Signature Conditioning](https://mlsanity.com/paper/2603.08998/) (2026)
- [Authentication of Copy Detection Patterns via Cross-Camera Dual-Synthetic Referencing](https://mlsanity.com/paper/2605.31292/) (2026)
- [Diffusion-Based Multi-Class Normality for OOD Detection: An Application to CDP Authentication](https://mlsanity.com/paper/2607.00609/) (2026)

## Abstract (arXiv)

This paper explores the potential of synthetic physical Copy Detection Patterns (CDP) to improve the robustness of anti-counterfeiting systems. By leveraging synthetic physical CDP, we aim at enhancing security and cost-effectiveness across various real-world applications. Our research demonstrates that synthetic CDP offer substantial improvements in authentication accuracy compared to one based on traditional digital templates. We conducted extensive tests using both a scanner and a diverse range of mobile phones, validating our approach through ROC analysis. The results indicate that synthetic CDP can reliably differentiate between original and fake samples, making this approach a viable solution for real-world applications, though requires an additional research to make this technology scalable across a variety of imaging devices.