NeurIPS 2025

An Evidence-Based Post-Hoc Adjustment Framework
for Anomaly Detection Under Data Contamination
Sukanya Patra
Department of Computer Science
University of Mons
Mons, Belgium
sukanya.patra@umons.ac.be
Souhaib Ben Taieb∗
Department of Statistics and Data Science
Mohamed bin Zayed University of Artificial Intelligence
Abu Dhabi, United Arab Emirates
souhaib.bentaieb@mbzuai.ac.ae
Abstract
Unsupervised anomaly detection (AD) methods typically assume clean training
data, yet real-world datasets often contain undetected or mislabeled anomalies,
leading to significant performance degradation. Existing solutions require access
to the training pipelines, data or prior knowledge of the proportions of anomalies
in the data, limiting their real-world applicability. To address this challenge, we
propose EPHAD, a simple yet effective test-time adaptation framework that updates
the outputs of AD models trained on contaminated datasets using evidence gath-
ered at test time. Our approach integrates the prior knowledge captured by the
AD model trained on contaminated datasets with evidence derived from multi-
modal foundation models like Contrastive Language-Image Pre-training (CLIP),
classical AD methods like the Local Outlier Factor or domain-specific knowledge.
We illustrate the intuition behind EPHAD using a synthetic toy example and vali-
date its effectiveness through comprehensive experiments across eight visual AD
datasets, twenty-six tabular AD datasets, and a real-world industrial AD dataset.
Additionally, we conduct an ablation study to analyse hyperparameter influence
and robustness to varying contamination levels, demonstrating the versatility and
robustness of EPHAD across diverse AD models and evidence pairs. To ensure
reproducibility, our code is publicly available2.
1
Introduction
Anomaly detection (AD) is the basis of many critical applications, including cybersecurity (Xiao
et al., 2024; Li et al., 2023a), healthcare (Bijlani et al., 2024; Huang et al., 2024), and i...