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

• URL: http://arxiv.org/abs/2603.08998v1
• Date: Mon, 09 Mar 2026 22:33:44 GMT
• Title: Diffusion-Based Authentication of Copy Detection Patterns: A Multimodal Framework with Printer Signature Conditioning
• Abstract summary: Formulating authentication as multi-class printer classification over printer signatures lets our model capture fine-grained, device-specific features.<n>We extend ControlNet by repurposing the denoising process for class-conditioned noise prediction.<n>Results show the framework generalises to counterfeit types unseen during training.
• Score: 24.87648457099111
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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