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

TL;DR

This paper introduces a diffusion-based multimodal framework for authenticating copy detection patterns by leveraging printer signatures and original templates, effectively distinguishing genuine prints from high-quality counterfeits even with advanced printing and scanning technologies.

Contribution

It proposes a novel diffusion model that incorporates printer signatures and the original template for improved authentication of copy detection patterns, outperforming existing methods.

Findings

Outperforms traditional similarity metrics and prior deep learning approaches.

Generalizes to unseen counterfeit types.

Effective in distinguishing high-quality counterfeits.

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.