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Diffusion-Based Authentication of Copy Detection Patterns: A Multimodal Framework with Printer Signature Conditioning

Bolutife Atoki, Iuliia Tkachenko, Bertrand Kerautret, Carlos Crispim-Junior

TL;DR

This work proposes a diffusion-based authentication framework that jointly leverages the original binary template, the printed CDP, and a representation of printer identity that captures relevant semantic information, and shows the framework generalises to counterfeit types unseen during training.

Abstract

Counterfeiting affects diverse industries, including pharmaceuticals, electronics, and food, posing serious health and economic risks. Printable unclonable codes, such as Copy Detection Patterns (CDPs), are widely used as an anti-counterfeiting measure and are applied to products and packaging. However, the increasing availability of high-resolution printing and scanning devices, along with advances in generative deep learning, undermines traditional authentication systems, which often fail to distinguish high-quality counterfeits from genuine prints. In this work, we propose a diffusion-based authentication framework that jointly leverages the original binary template, the printed CDP, and a representation of printer identity that captures relevant semantic information. Formulating authentication as multi-class printer classification over printer signatures lets our model capture fine-grained, device-specific features via spatial and textual conditioning. We extend ControlNet by repurposing the denoising process for class-conditioned noise prediction, enabling effective printer classification. On the Indigo 1 x 1 Base dataset, our method outperforms traditional similarity metrics and prior deep learning approaches. Results show the framework generalises to counterfeit types unseen during training.

Diffusion-Based Authentication of Copy Detection Patterns: A Multimodal Framework with Printer Signature Conditioning

TL;DR

This work proposes a diffusion-based authentication framework that jointly leverages the original binary template, the printed CDP, and a representation of printer identity that captures relevant semantic information, and shows the framework generalises to counterfeit types unseen during training.

Abstract

Counterfeiting affects diverse industries, including pharmaceuticals, electronics, and food, posing serious health and economic risks. Printable unclonable codes, such as Copy Detection Patterns (CDPs), are widely used as an anti-counterfeiting measure and are applied to products and packaging. However, the increasing availability of high-resolution printing and scanning devices, along with advances in generative deep learning, undermines traditional authentication systems, which often fail to distinguish high-quality counterfeits from genuine prints. In this work, we propose a diffusion-based authentication framework that jointly leverages the original binary template, the printed CDP, and a representation of printer identity that captures relevant semantic information. Formulating authentication as multi-class printer classification over printer signatures lets our model capture fine-grained, device-specific features via spatial and textual conditioning. We extend ControlNet by repurposing the denoising process for class-conditioned noise prediction, enabling effective printer classification. On the Indigo 1 x 1 Base dataset, our method outperforms traditional similarity metrics and prior deep learning approaches. Results show the framework generalises to counterfeit types unseen during training.
Paper Structure (13 sections, 4 equations, 2 figures, 6 tables)

This paper contains 13 sections, 4 equations, 2 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Previous Work
  3. Methodology
  4. Definitions
  5. Diffusion-Based Signature Classification
  6. Forward diffusion:
  7. Reverse diffusion:
  8. Framework components
  9. Experiments
  10. Experimental details
  11. Results and Discussion
  12. Conclusion
  13. Acknowledgements

Figures (2)

  • Figure 1: Examples of a binary template, authentic printed CDP, and counterfeit printed CDPs from traditional image processing and deep learning methods. Normalised cross-correlation reliably detects traditional counterfeits but is unreliable against deep learning-based ones.
  • Figure 2: Authentication pipeline leveraging printer signature reconstruction. The reverse diffusion process takes a noised template as input and is conditioned on the printed CDP and printer identity text. ControlNet is modified to predict noise per class and includes a classification module selecting the class with minimal prediction error.