When Normal Looks Anomalous: Towards Decoration-Robust Industrial Anomaly Detection

Many food products carry a decoration that is placed at random and is always normal, such as sesame seeds on a burger bun, while the defects that matter lie on the surface underneath. We call this setting stochastic normal decoration and introduce a benchmark for it: plain loaves with and without synthetic sesame placed by a model fitted to real buns, so that every defective loaf exists in a paired form, together with real sesame buns. On it, PatchCore loses most of its detectability and its heat concentrates on the seeds. A 2x2 swap of memory bank and test image shows why: at the defect pixels that plain PatchCore finds, sesame leaves the nearest-neighbour distance unchanged; it raises the distance of normal surface, most strongly next to seeds. We therefore propose decoration-context normalisation, which standardises each pixel's PatchCore distance against normal pixels that share its context (distance to the nearest detected seed, local seed density, distance to the region edge), using normal calibration images outside the memory bank. It needs no defect labels or retraining and removes no pixel. With calibration units physically disjoint from bank and test, it raises image AUROC on the paired set from 0.768 to 0.920 (all six loaves improve) and on real buns from 0.894 to 0.945 (four of five buns; not resolved with five buns). Score-level masking of detected seeds reaches numerically similar AUROC but stops inspecting up to 30% of the bun and, in a stress test, detects white seed-shaped foreign matter less well; both fail on piles of seeds. In a synthetic black-sesame transfer test the method again improves every loaf over PatchCore, while masking is better when the decoration detector also picks up dark defects.