Anomaly synthesis strategies can effectively enhance unsupervised anomaly detection. However, existing strategies have limitations in the coverage and controllability of anomaly synthesis, particularly for weak defects that are very similar to normal regions. In this paper, we propose Global and Local Anomaly co-Synthesis Strategy (GLASS), a novel unified framework designed to synthesize a broader coverage of anomalies under the manifold and hypersphere distribution constraints of Global Anomaly Synthesis (GAS) at the feature level and Local Anomaly Synthesis (LAS) at the image level. Our method synthesizes near-in-distribution anomalies in a controllable way using Gaussian noise guided by gradient ascent and truncated projection. GLASS achieves state-of-the-art results on the MVTec AD (detection AUROC of 99.9\%), VisA, and MPDD datasets and excels in weak defect detection. The effectiveness and efficiency have been further validated in industrial applications for woven fabric defect detection. The code and dataset are available at: \url{https://github.com/cqylunlun/GLASS}.

翻译：异常合成策略能有效增强无监督异常检测。然而，现有策略在异常合成的覆盖范围与可控性方面存在局限，尤其对于与正常区域极为相似的微弱缺陷。本文提出全局与局部异常协同合成策略（GLASS），这是一种新颖的统一框架，旨在特征层级的全局异常合成（GAS）的流形与超球分布约束以及图像层级的局部异常合成（LAS）约束下，合成覆盖范围更广的异常。我们的方法通过梯度上升引导的高斯噪声与截断投影，以可控方式合成接近分布内的异常。GLASS在MVTec AD（检测AUROC达99.9%）、VisA和MPDD数据集上取得了最先进的结果，并在微弱缺陷检测方面表现优异。其有效性和效率已在工业织物缺陷检测应用中进一步验证。代码与数据集发布于：\url{https://github.com/cqylunlun/GLASS}。