Robust video anomaly detection via causal feature-guided data augmentation

Data augmentation is a commonly used technique to learn distinguishing features for accurately identifying video anomaly patterns. However, traditional data augmentation methods introduce spurious features-spurious correlations (e.g. , lighting conditions) that lack causal links to anomalies, yielding models that excel on specific test sets but fail in real-world scenarios with varying conditions. In this paper, we propose a novel robust video anomaly detection method by designing a causal feature-guided augmentation technique that strategically enhances the learnability of predictive patterns while suppressing spurious correlations. Specifically, we first disentangle causal features , i.e. , directly predictive of labels, from spurious features via a causal generative model. We then perturb causal features to enhance their variability in training data, compelling the model to focus on invariant patterns while ignoring spurious correlations. The augmented samples are reconstructed with the refined causal representations, enhancing the model’s discriminative capability. Furthermore, we introduce a systematic evaluation framework with three increasing difficulty levels to assess robustness: seen/unseen variations, cross-dataset generalization, and cross-domain adaptation. This evaluation examines system stability under varying conditions, closely aligning with real-world surveillance deployment requirements. Experimental results validate both the effectiveness and robustness of our method.