Diffusion Counterfactual-Based Anomaly Detection in Class-Imbalanced Data

Abstract

Anomaly detection suffers from data imbalance, as anomalies are typically rare. Due to the smaller number of anomaly samples, models may overfit the features of the normal samples, resulting in poor performance when detecting anomalies. Historically, this issue has typically been addressed by undersampling normal data or oversampling anomalous data. Most methods have a significant drawback when oversampling the anomalous data: a lack of interpretability. Synthesized anomalous data often lack the background and contextual information of real data, making it difficult to interpret the reasons behind their synthesis. This lack of underlying reasons for synthesis can prevent the application of such methods in critical anomaly detection domains, such as medical diagnosis and autonomous driving. Therefore, we propose a Diffusion Counterfactual-Based Anomaly Detection (DCFAD) model to generate more interpretable anomalous data, addressing the issue of the scarcity of anomalous data. Through extensive experiments, the effectiveness of the proposed DCFAD model was validated on multiple datasets across two metrics.