针对女性安全设计的多模态异常检测数据集，填补了现有研究空白。

Women's safety and security are paramount for a modern society. Often, crimes scenes get recorded through low-resolution CCTV cameras limiting the efficiency of video anomaly detection (VAD) models. Despite substantial progress in VAD research, women-centric anomalies are still underrepresented in datasets as well as in models. Existing datasets primarily cover well-lit, high-resolution and close-shot videos that are inadequate to tackle critical anomalies such as chain snatching, stalking, inappropriate touch, and other subtle forms of crime against women. To address this, we present a new benchmark, referred to as ExtrAnom. It contains 1001 videos (both anomalies and normal) with four textual annotations; one human-generated and three LLM-generated. The videos are arranged in 5 different categories of crimes. The dataset comprises low-light (8%), low-resolution (13%), long-shot (15%), and daytime (64%) anomaly videos. It includes stalking (3.9%), chain snatching (17.6%), kidnapping (7.3%), assassinations (2.3%), harassment (18.9%), and normal (50%) videos. It is possible to perform cross-modal and VLM-based validations using ExtrAnom. We have benchmarked it against popular unimodal and multi-modal VAD datasets (e.g., XD-Violence, UCF-Crime, and UCA) and SOTA methods. Experiments reveal that existing datasets are insufficient to deal with women-centric anomalies. We believe ExtrAnom can fill this critical gap in VAD research.