HDM: Hybrid Diffusion Model for Unified Image Anomaly Detection

TL;DR

The paper introduces HDM, a hybrid diffusion model that unifies anomaly sample generation and detection, significantly enhancing industrial image anomaly detection accuracy at both image and pixel levels.

Contribution

The novel HDM framework integrates generation and discrimination tasks into a single diffusion-based model, improving anomaly detection performance over existing methods.

Findings

Outperforms state-of-the-art methods on industrial datasets

Achieves higher AUROC scores for image and pixel-level detection

Effectively captures diverse anomaly patterns

Abstract

Image anomaly detection plays a vital role in applications such as industrial quality inspection and medical imaging, where it directly contributes to improving product quality and system reliability. However, existing methods often struggle with complex and diverse anomaly patterns. In particular, the separation between generation and discrimination tasks limits the effective coordination between anomaly sample generation and anomaly region detection. To address these challenges, we propose a novel hybrid diffusion model (HDM) that integrates generation and discrimination into a unified framework. The model consists of three key modules: the Diffusion Anomaly Generation Module (DAGM), the Diffusion Discriminative Module (DDM), and the Probability Optimization Module (POM). DAGM generates realistic and diverse anomaly samples, improving their representativeness. DDM then applies a reverse diffusion process to capture the differences between generated and normal samples, enabling precise anomaly region detection and localization based on probability distributions. POM refines the probability distributions during both the generation and discrimination phases, ensuring high-quality samples are used for training. Extensive experiments on multiple industrial image datasets demonstrate that our method outperforms state-of-the-art approaches, significantly improving both image-level and pixel-level anomaly detection performance, as measured by AUROC.