# Stochastic Digital Twin for Copy Detection Patterns

arXiv: https://arxiv.org/abs/2309.16866 · HTML: https://mlsanity.com/paper/2309.16866/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 8 of 8)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Understanding Diffusion Models: A Unified Perspective](https://mlsanity.com/paper/2208.11970/) (2022)
- [Mathematical model of printing-imaging channel for blind detection of fake copy detection patterns](https://mlsanity.com/paper/2212.07326/) (2022)
- [Digital twins of physical printing-imaging channel](https://mlsanity.com/paper/2210.17420/) (2022)
- [Printing variability of copy detection patterns](https://mlsanity.com/paper/2210.05343/) (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 (CDP) present an efficient technique for product protection against counterfeiting. However, the complexity of studying CDP production variability often results in time-consuming and costly procedures, limiting CDP scalability. Recent advancements in computer modelling, notably the concept of a "digital twin" for printing-imaging channels, allow for enhanced scalability and the optimization of authentication systems. Yet, the development of an accurate digital twin is far from trivial. This paper extends previous research which modelled a printing-imaging channel using a machine learning-based digital twin for CDP. This model, built upon an information-theoretic framework known as "Turbo", demonstrated superior performance over traditional generative models such as CycleGAN and pix2pix. However, the emerging field of Denoising Diffusion Probabilistic Models (DDPM) presents a potential advancement in generative models due to its ability to stochastically model the inherent randomness of the printing-imaging process, and its impressive performance in image-to-image translation tasks. This study aims at comparing the capabilities of the Turbo framework and DDPM on the same CDP datasets, with the goal of establishing the real-world benefits of DDPM models for digital twin applications in CDP security. Furthermore, the paper seeks to evaluate the generative potential of the studied models in the context of mobile phone data acquisition. Despite the increased complexity of DDPM methods when compared to traditional approaches, our study highlights their advantages and explores their potential for future applications.