Diffusion-Based Multi-Class Normality for OOD Detection: An Application to CDP Authentication
This work addresses the practical problem of authenticating copy detection patterns in anti-counterfeiting, where multiple printing-and-digitisation classes must be handled by a single detector.
The paper tackles multi-class out-of-distribution detection for copy detection pattern authentication using a diffusion-based framework. The proposed method achieves superior performance over baselines on the Indigo 1x1 Base dataset, without using counterfeit samples for training or threshold calibration.
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.