A Survey on Diffusion Models for Anomaly Detection
Journal:
arXiv
Published Date:
Jan 20, 2025
Abstract
Diffusion models (DMs) have emerged as a powerful class of generative AI
models, showing remarkable potential in anomaly detection (AD) tasks across
various domains, such as cybersecurity, fraud detection, healthcare, and
manufacturing. The intersection of these two fields, termed diffusion models
for anomaly detection (DMAD), offers promising solutions for identifying
deviations in increasingly complex and high-dimensional data. In this survey,
we review recent advances in DMAD research. We begin by presenting the
fundamental concepts of AD and DMs, followed by a comprehensive analysis of
classic DM architectures including DDPMs, DDIMs, and Score SDEs. We further
categorize existing DMAD methods into reconstruction-based, density-based, and
hybrid approaches, providing detailed examinations of their methodological
innovations. We also explore the diverse tasks across different data
modalities, encompassing image, time series, video, and multimodal data
analysis. Furthermore, we discuss critical challenges and emerging research
directions, including computational efficiency, model interpretability,
robustness enhancement, edge-cloud collaboration, and integration with large
language models. The collection of DMAD research papers and resources is
available at https://github.com/fdjingliu/DMAD.
