Zero-Shot Image Anomaly Detection Using Generative Foundation Models

• URL: http://arxiv.org/abs/2507.22692v1
• Date: Wed, 30 Jul 2025 13:56:36 GMT
• Title: Zero-Shot Image Anomaly Detection Using Generative Foundation Models
• Authors: Lemar Abdi, Amaan Valiuddin, Francisco Caetano, Christiaan Viviers, Fons van der Sommen,
• Abstract summary: This research explores the use of score-based generative models as foundational tools for semantic anomaly detection.<n>By analyzing Stein score errors, we introduce a novel method for identifying anomalous samples without requiring re-training on each target dataset.<n>Our approach improves over state-of-the-art and relies on training a single model on one dataset -- CelebA -- which we find to be an effective base distribution.
• Score: 2.241618130319058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting out-of-distribution (OOD) inputs is pivotal for deploying safe vision systems in open-world environments. We revisit diffusion models, not as generators, but as universal perceptual templates for OOD detection. This research explores the use of score-based generative models as foundational tools for semantic anomaly detection across unseen datasets. Specifically, we leverage the denoising trajectories of Denoising Diffusion Models (DDMs) as a rich source of texture and semantic information. By analyzing Stein score errors, amplified through the Structural Similarity Index Metric (SSIM), we introduce a novel method for identifying anomalous samples without requiring re-training on each target dataset. Our approach improves over state-of-the-art and relies on training a single model on one dataset -- CelebA -- which we find to be an effective base distribution, even outperforming more commonly used datasets like ImageNet in several settings. Experimental results show near-perfect performance on some benchmarks, with notable headroom on others, highlighting both the strength and future potential of generative foundation models in anomaly detection.

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