  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}.
