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

2026-03-09

AI Summary

Traditional anti-counterfeiting systems struggle to defend against high-fidelity replicas and generative AI-based attacks. This work proposes the first diffusion model–based multimodal authentication framework, formulating anti-counterfeiting as a multiclass classification problem that jointly leverages the original binary template, post-print copy detection patterns (CDPs), and printer identity signatures. By incorporating printer-specific signatures as semantic conditions and enhancing ControlNet to support class-conditional noise prediction, the method enables fine-grained device feature extraction guided jointly by spatial and textual cues. Evaluated on the Indigo 1×1 Base dataset, the approach significantly outperforms existing deep learning and conventional similarity-based methods, demonstrating strong generalization to unseen forgery types.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• counterfeit detection
• Copy Detection Patterns
• printer authentication
• anti-counterfeiting
• generative deep learning

Innovation

• diffusion-based authentication
• Copy Detection Patterns
• printer signature
• ControlNet
• multimodal framework