# Provable Performance Guarantees of Copy Detection Patterns

arXiv: https://arxiv.org/abs/2409.17649 · HTML: https://mlsanity.com/paper/2409.17649/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [Clonability of anti-counterfeiting printable graphical codes: a machine learning approach](https://mlsanity.com/paper/1903.07359/) (2019)
- [Mathematical model of printing-imaging channel for blind detection of fake copy detection patterns](https://mlsanity.com/paper/2212.07326/) (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)

## Built on by (most cited first, 1 of 1)

- [Diffusion-Based Multi-Class Normality for OOD Detection: An Application to CDP Authentication](https://mlsanity.com/paper/2607.00609/) (2026)

## Abstract (arXiv)

Copy Detection Patterns (CDPs) are crucial elements in modern security applications, playing a vital role in safeguarding industries such as food, pharmaceuticals, and cosmetics. Current performance evaluations of CDPs predominantly rely on empirical setups using simplistic metrics like Hamming distances or Pearson correlation. These methods are often inadequate due to their sensitivity to distortions, degradation, and their limitations to stationary statistics of printing and imaging. Additionally, machine learning-based approaches suffer from distribution biases and fail to generalize to unseen counterfeit samples. Given the critical importance of CDPs in preventing counterfeiting, including the counterfeit vaccines issue highlighted during the COVID-19 pandemic, there is an urgent need for provable performance guarantees across various criteria. This paper aims to establish a theoretical framework to derive optimal criteria for the analysis, optimization, and future development of CDP authentication technologies, ensuring their reliability and effectiveness in diverse security scenarios.