Stochastic Digital Twin for Copy Detection Patterns

TL;DR

This paper compares the Turbo information-theoretic digital twin framework with Denoising Diffusion Probabilistic Models (DDPM) for copy detection pattern security, highlighting DDPM's potential for improved stochastic modeling and generative capabilities.

Contribution

It extends previous digital twin models by evaluating DDPM against Turbo for CDP, demonstrating potential advantages of DDPM in security and mobile data acquisition contexts.

Findings

DDPM shows superior stochastic modeling of printing-imaging variability.

DDPM outperforms Turbo in image-to-image translation tasks.

Potential for DDPM to enhance CDP security applications.

Abstract

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.