A Survey on Industrial Anomaly Synthesis

Abstract: This paper presents a comprehensive review of industrial anomaly synthesis (IAS). Existing surveys on industrial anomalies mainly focus on anomaly detection, while anomaly synthesis is typically treated as an auxiliary component rather than as an independent topic. However, owing to its increasing importance in data augmentation, downstream model training, and controllable industrial inspection, IAS has become a research direction of growing interest. To address the lack of a dedicated review, we survey a broad range of representative methods and organize them into four paradigms: hand-crafted synthesis, distribution hypothesis-based synthesis, generative model (GM)-based synthesis, and vision-language model (VLM)-based synthesis. We further establish a dedicated taxonomy for IAS, which supports more systematic comparison across methods and offers a clearer view of the field’s development. Beyond methodological categorization, we summarize the datasets, benchmarks, and evaluation metrics commonly adopted in IAS, and review recent advances in multimodal anomaly synthesis that remain underexplored in prior surveys. Overall, this survey provides a structured understanding of existing IAS methods, evaluation settings, current limitations, and promising future directions, and is intended to serve as a reference for subsequent research in this area. More resources are available at https://anonymous.4open.science/status/IAS.

Submission Type: Long submission (more than 12 pages of main content)

Assigned Action Editor: ~Fuxin_Li1

Submission Number: 8637