Generating Plausible Counterfactual Instances for Anomaly Interpretation with Generative Adversarial Networks

Anomaly interpretation plays a critical role in understanding abnormal behaviors within high-stakes decision systems. Counterfactual Explanation (CE), an emerging technique, offers promise by generating hypothetical scenarios that transition anomalies to normal states through minimal perturbations. However, when applied specifically to anomaly interpretation, existing CE methods suffer from three critical limitations: (i) Distance-minimizing CE methods produce counterfactual instances clustered near anomalies, failing to reach core normal regions; (ii) Gradient-based CE methods suffer geometric instability near complex boundaries where anomalies reside; (iii) CE methods trained solely on normal instances generate out-of-distribution counterfactual instances from anomalies. To address these issues, we propose GLEAN ( G enerating p L ausible count E rfactual instances for A nomaly i N terpretation with generative adversarial networks), a novel framework leveraging generative adversarial networks to generate plausible and in-distribution counterfactual explanations specifically for anomaly interpretation. Technically, GLEAN employs adversarial perturbation to transform anomalies into plausible counterfactual instances within the normal instance distribution, guided by normal instance prototypes to ensure interpretability and reduce sensitivity to the decision boundary. The framework establishes an adversarial equilibrium where a generator minimizes feature perturbations, the distance between generated counterfactual instances and query anomalies, and the similarity between generated counterfactual instances and inlier prototypes, while a density-based discriminator to ensure that generated counterfactual instances adhere to the normal data distribution. GLEAN outputs the most plausible and in-distribution counterfactual instances as explanations for the anomalies identified. Empirical results show that GLEAN achieves state-of-the-art performance across four key metrics of Density, Distance, Proximity and Sparsity, outperforming 15 baseline methods on ten real-world datasets. Our datasets and source code are publicly available at https://github.com/KDDElab/GLEAN.