{"type":"ScholarlyArticle","title":"Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling","authors":[{"name":"Di Wu","slug":"di-wu","given":"Di","family":"Wu","affiliation":"School of Automation Engineering , University of Electronic Science and Technology of China(UESTC)"},{"name":"Shicai Fan","slug":"shicai-fan","given":"Shicai","family":"Fan","affiliation":"School of Automation Engineering , University of Electronic Science and Technology of China(UESTC); Shenzhen Institute for Advanced Study , UESTC"},{"name":"Xue Zhou","slug":"xue-zhou","given":"Xue","family":"Zhou","affiliation":"School of Automation Engineering , University of Electronic Science and Technology of China(UESTC); Shenzhen Institute for Advanced Study , UESTC"},{"name":"Li Yu","slug":"li-yu","given":"Li","family":"Yu","affiliation":"School of Automation Engineering , University of Electronic Science and Technology of China(UESTC); Shenzhen Institute for Advanced Study , UESTC"},{"name":"Yuzhong Deng","slug":"yuzhong-deng","given":"Yuzhong","family":"Deng","affiliation":"School of Automation Engineering , University of Electronic Science and Technology of China(UESTC)"},{"name":"Jianxiao Zou","slug":"jianxiao-zou","given":"Jianxiao","family":"Zou","affiliation":"School of Automation Engineering , University of Electronic Science and Technology of China(UESTC); Shenzhen Institute for Advanced Study , UESTC"},{"name":"Baihong Lin","slug":"baihong-lin","given":"Baihong","family":"Lin","affiliation":"Shenzhen Institute for Advanced Study , UESTC"}],"abstract":"Reconstruction-based methods have been commonly used for unsupervised anomaly detection, in which a normal image is reconstructed and compared with the given test image to detect and locate anomalies. Recently, diffusion models have shown promising applications for anomaly detection due to their powerful generative ability. However, these models lack strict mathematical support for normal image reconstruction and unexpectedly suffer from low reconstruction quality. To address these issues, this paper proposes a novel and highly-interpretable method named Masked Diffusion Posterior Sampling (MDPS). In MDPS, the problem of normal image reconstruction is mathematically modeled as multiple diffusion posterior sampling for normal images based on the devised masked noisy observation model and the diffusion-based normal image prior under Bayesian framework. Using a metric designed from pixel-level and perceptual-level perspectives, MDPS can effectively compute the difference map between each normal posterior sample and the given test image. Anomaly scores are obtained by averaging all difference maps for multiple posterior samples. Exhaustive experiments on MVTec and BTAD datasets demonstrate that MDPS can achieve state-of-the-art performance in normal image reconstruction quality as well as anomaly detection and localization.","doi":"10.24963/ijcai.2024/270","url":"https://ijcai.org/proceedings/2024/270","pdf":"https://ijcai.org/proceedings/2024/0270.pdf","markdown":"https://ijcai.org/proceedings/2024/270.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/270","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":270,"pages":{"first":2442,"last":2450},"keywords":["Data Mining: DM: Anomaly/outlier detection","Computer Vision: CV: Image and video synthesis and generation","Computer Vision: CV: Applications"],"videos":[],"references":[{"position":1,"raw":"[Akc ¸ay et al., 2019] Samet Akc ¸ay, Amir Atapour- Abarghouei, and Toby P Breckon. 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