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

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 directly 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 semantically meaningful representation of printer identity. By formulating authentication as a multi-class classification task over printer signatures, our model captures fine-grained, device-specific features through both spatial and textual conditioning. We extend ControlNet by repurposing the denoising process for class-conditioned noise prediction, enabling effective printer classification. Experiments on the Indigo 1 x 1 Base dataset show that our method outperforms traditional similarity metrics and prior deep learning approaches. Results further demonstrate that the framework generalises robustly to counterfeit types not seen during training.