UNEVEN NORMAL COVERAGE PERTURBS RESIDUAL ANOMALY METRICS: A SPECTRAL MECHANISM STUDY

Residual anomaly detectors rely on reference statistics from normal data, making global covariance-based scores vulnerable to unequal coverage of valid normal modes. We present a mechanistic study linking uneven normal-reference coverage to residual-metric changes and target-specific false positive inflation. By isolating the metric pathway, we use a two-mode mixture model to separate within-mode covariance shifts from between-mode mean differences, along with an exact common-basis decomposition to attribute score changes to specific spectral directions. In the primary DINOv3 ViT-B/16 representation, positive net attribution is heavily concentrated in leading spectral directions. Controlled interventions—including sample-matched deletions, reweighting, and spectral adjustments—confirm that these shifts stem from composition and spectral location rather than sample count. As a mechanism-inspired probe, Head-Calibrated Mahalanobis (HCM) mitigates conditional false-positive inflation under reduced coverage, demonstrating that the identified failure mode is actionable. Our work provides a precise mechanistic account of coverage sensitivity in residual anomaly detection.