FLASH: A "Generate Once, Synthesize Many" Framework for Synthetic Anomaly Generation in Industrial Anomaly Detection

2026-09-29

AI Summary

This study addresses the scarcity of real defects and the inefficiency and limited diversity of existing synthesis methods in industrial anomaly detection. We propose a "generate once, synthesize multiple times" paradigm that introduces a novel decoupling mechanism for defect extraction and synthesis. By integrating vision-language model guidance with image generation techniques, our approach achieves precise defect localization and seamless blending through object boundary suppression and multi-resolution spectral pyramid noise, enabling rapid construction of large-scale datasets via patch reuse. Evaluated on MVTec AD 2, the method attains an F1 score of 78.1%, approaching the upper bound of real-data performance, while accelerating synthesis by over 11.95×. Furthermore, it significantly improves calibration-transfer consistency, demonstrating its effectiveness for scalable and high-fidelity industrial anomaly detection.

Abstract

Synthetic anomaly generation helps expand industrial anomaly datasets when real defects are scarce or unavailable. Existing approaches lie at two extremes: procedural approaches are fast but struggle to represent complex anomalies, while generative approaches produce diverse defects but require costly per-sample generation. We present FLASH, a framework that decouples defect generation from anomaly synthesis under a ``generate once, synthesize many'' paradigm. Given only normal images, FLASH uses Vision-Language Model (VLM) guidance and an image-generation model to produce a small set of defect images, from which it extracts, validates, and banks reusable defect patches. For synthesis of anomalous images, Object Boundary Suppression (OBS) first identifies the probable foreground object-aware region of the host image, while Multi-Resolution Spectral Pyramid (MRSP) noise generates diverse, size-controllable masks that determine the defect location and spatial extent. It then composes a large and diverse synthetic anomalous image set by localizing the defect region, sampling size-controllable placement masks and seamlessly blending retrieved defects onto new defect-free images without further need for image generation. Experiments on the MVTec AD 2 dataset show that FLASH-generated anomalies nearly close the calibration gap on real defects, reaching 78.1% image-level F1 against an 83.6% real-anomaly upper bound and providing the most consistent calibration transfer across detectors among procedural and generative alternatives. Moreover, FLASH synthesizes anomalies more than 11.95x faster than per-sample generative approaches.

Problem

• Industrial Anomaly Detection
• Synthetic Anomaly Generation
• Defect Synthesis
• Data Augmentation

Innovation

• Synthetic Anomaly Generation
• Vision-Language Model
• Object Boundary Suppression
• Multi-Resolution Spectral Pyramid
• Defect Patch Bank