用扩散模型结合打印机特征，精准识别印刷防伪码真伪。
Diffusion-Based Authentication of Copy Detection Patterns: A Multimodal Framework with Printer Signature Conditioning
- 通过打印机签名与图文双重条件控制扩散过程，捕捉设备特异性特征。
- 在Indigo数据集上超越传统方法，对未见过的伪造类型仍有效。
- 适合需要高安全性的防伪场景，如药品、电子品包装验证。
造假影响制药、电子、食品等多个行业，带来严重健康与经济风险。可打印不可克隆代码（如复制检测图案CDPs）被广泛用于产品和包装防伪。然而，高分辨率打印扫描设备及生成式深度学习的发展，使得传统认证系统难以区分高质量伪造品与真品。本文提出一种基于扩散模型的认证框架，联合使用原始二值模板、印刷后的CDP以及体现打印机身份的语义表示。将认证建模为基于打印机签名的多类分类问题，通过空间与文本条件控制，让模型捕捉细微的设备特异性特征。我们改进ControlNet，将去噪过程重用于类别条件噪声预测，实现高效打印机分类。在Indigo 1 x 1 Base数据集上，该方法优于传统相似性度量与先前深度学习方法。结果表明，该框架能泛化到训练中未见的伪造类型。
原文摘要 · Abstract (English)
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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