# Diffusion-Based Multi-Class Normality for OOD Detection: An Application to CDP Authentication

arXiv: https://arxiv.org/abs/2607.00609 · HTML: https://mlsanity.com/paper/2607.00609/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 22 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 16 of 16)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Your Diffusion Model is Secretly a Zero-Shot Classifier](https://mlsanity.com/paper/2303.16203/) (2023)
- [Denoising diffusion models for out-of-distribution detection](https://mlsanity.com/paper/2211.07740/) (2022)
- [Unsupervised Out-of-Distribution Detection with Diffusion Inpainting](https://mlsanity.com/paper/2302.10326/) (2023)
- [Source Printer Classification using Printer Specific Local Texture Descriptor](https://mlsanity.com/paper/1806.06650/) (2018)
- [Mathematical model of printing-imaging channel for blind detection of fake copy detection patterns](https://mlsanity.com/paper/2212.07326/) (2022)
- [Mobile authentication of copy detection patterns](https://mlsanity.com/paper/2203.02397/) (2022)
- [Digital twins of physical printing-imaging channel](https://mlsanity.com/paper/2210.17420/) (2022)
- [Assessing the Viability of Synthetic Physical Copy Detection Patterns on Different Imaging Systems](https://mlsanity.com/paper/2410.02575/) (2024)
- [Anomaly localization for copy detection patterns through print estimations](https://mlsanity.com/paper/2209.15625/) (2022)
- [Stochastic Digital Twin for Copy Detection Patterns](https://mlsanity.com/paper/2309.16866/) (2023)
- [Provable Performance Guarantees of Copy Detection Patterns](https://mlsanity.com/paper/2409.17649/) (2024)
- [Diffusion-Based Authentication of Copy Detection Patterns: A Multimodal Framework with Printer Signature Conditioning](https://mlsanity.com/paper/2603.08998/) (2026)

## Abstract (arXiv)

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.