Anomaly Detection and Generation with Diffusion Models: A Survey
Journal:
arXiv
Published Date:
Jun 11, 2025
Abstract
Anomaly detection (AD) plays a pivotal role across diverse domains, including
cybersecurity, finance, healthcare, and industrial manufacturing, by
identifying unexpected patterns that deviate from established norms in
real-world data. Recent advancements in deep learning, specifically diffusion
models (DMs), have sparked significant interest due to their ability to learn
complex data distributions and generate high-fidelity samples, offering a
robust framework for unsupervised AD. In this survey, we comprehensively review
anomaly detection and generation with diffusion models (ADGDM), presenting a
tutorial-style analysis of the theoretical foundations and practical
implementations and spanning images, videos, time series, tabular, and
multimodal data. Crucially, unlike existing surveys that often treat anomaly
detection and generation as separate problems, we highlight their inherent
synergistic relationship. We reveal how DMs enable a reinforcing cycle where
generation techniques directly address the fundamental challenge of anomaly
data scarcity, while detection methods provide critical feedback to improve
generation fidelity and relevance, advancing both capabilities beyond their
individual potential. A detailed taxonomy categorizes ADGDM methods based on
anomaly scoring mechanisms, conditioning strategies, and architectural designs,
analyzing their strengths and limitations. We final discuss key challenges
including scalability and computational efficiency, and outline promising
future directions such as efficient architectures, conditioning strategies, and
integration with foundation models (e.g., visual-language models and large
language models). By synthesizing recent advances and outlining open research
questions, this survey aims to guide researchers and practitioners in
leveraging DMs for innovative AD solutions across diverse applications.
