PRISM: Training-Free Video Anomaly Detection via Intrinsic Statistical Modeling

While recent training-free video anomaly detection (VAD) methods offer advantages such as interpretability and ease of deployment, they often suffer from computational inefficiency due to complex memory retrieval mechanisms or highlatency visual-language models (VLMs). To address this issue, we propose PRISM (Parameterless Recognition Based on Intrinsic Statistical Modeling), a novel framework for efficient openset anomaly detection with minimal computational cost. Built on a pre-trained multimodal embedding model, PRISM introduces differential amplification and whitening mechanisms to statistically suppress common-mode background noise in the embedding space, thereby improving the signal-to-noise ratio of anomalous events. Extensive experiments on three widely used datasets demonstrate that PRISM achieves state-of-the-art performance among training-free methods while maintaining real-time inference capability. Furthermore, our statistical analysis offers a complementary perspective on why training-free methods may suffer from lower Average Precision (AP) on complex datasets such as XD-Violence. Code is released at https://github.com/ytC2026/ICML2026-PRISM.