发表机构
CNRS; INSA Lyon; LIRIS, UMR 5205（法国国家科学研究中心; 里昂国立应用科学学院; LIRIS实验室，UMR 5205）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出扩散多类正态性框架，用单一条件ControlNet训练于多类真实CDP，通过重构误差检测伪造，并引入双模板掩码提升性能。
AI 中文摘要
基于重构的生成模型为无监督的分布外（OOD）检测提供了自然框架，但多类正态性建模需要单个检测器捕获多个类内分布流形，并跨类产生可比较的异常分数。我们在复制检测模式（CDP）认证中研究该问题，其中真实和伪造样本视觉相似，但在微妙的打印和数字化（P&D）特征上不同。我们提出一个基于扩散的多类正态性框架，其中单个类条件ControlNet仅在来自多个P&D类的真实CDP上训练，并通过在真实类条件下的重构误差检测伪造品。我们进一步引入双模板掩码，隐藏输入模板的互补区域并仅评分保留像素，减少对可见二进制结构的依赖。在Indigo 1 x 1 Base数据集上，所提方法在多类真实与伪造评估中优于传统和改编的生成基线，且无需使用伪造样本进行训练或阈值校准。
英文摘要
Reconstruction-based generative models offer a natural framework for unsupervised out-of-distribution (OOD) detection, but multi-class normality modelling requires a single detector to capture multiple in-distribution manifolds and produce comparable anomaly scores across classes. We study this problem in copy detection pattern (CDP) authentication, where authentic and counterfeit samples are visually similar but differ in subtle printing-and-digitisation (P&D) signatures. We propose a diffusion-based multi-class normality framework in which a single class-conditional ControlNet is trained exclusively on authentic CDPs from multiple P&D classes and detects counterfeits through reconstruction error under authentic-class conditioning. We further introduce dual template masking, which hides complementary regions of the input template and scores only withheld pixels, reducing reliance on visible binary structure. On the Indigo 1 x 1 Base dataset, the proposed method outperforms traditional and adapted generative baselines under multi-class authentic-versus-counterfeit evaluation, without using counterfeit samples for training or threshold calibration.
CommentsIEEE International Conference on Advanced Visual And Signal-Based Systems, Aug 2026, Lecce, Italy
