Sexual harassment, sexual abuse, and sexual violence are prevalent problems in this day and age. Women's safety is an important issue that needs to be highlighted and addressed. Given this issue, we have studied each of these concerns and the factors that affect it based on images generated from movies. We have classified the three terms (harassment, abuse, and violence) based on the visual attributes present in images depicting these situations. We identified that factors such as facial expression of the victim and perpetrator and unwanted touching had a direct link to identifying the scenes containing sexual harassment, abuse and violence. We also studied and outlined how state-of-the-art explicit content detectors such as Google Cloud Vision API and Clarifai API fail to identify and categorise these images. Based on these definitions and characteristics, we have developed a first-of-its-kind dataset from various Indian movie scenes. These scenes are classified as sexual harassment, sexual abuse, or sexual violence and exported in the PASCAL VOC 1.1 format. Our dataset is annotated on the identified relevant features and can be used to develop and train a deep-learning computer vision model to identify these issues. The dataset is publicly available for research and development.

翻译：性骚扰、性虐待和性暴力是当今普遍存在的问题。女性安全是一个需要重点强调和解决的重要议题。针对这一问题，我们基于电影生成的图像研究了各类相关行为及其影响因素。根据呈现这些情境的图像中的视觉属性，我们对三种术语（骚扰、虐待和暴力）进行了分类。研究发现，受害者和施害者的面部表情以及非自愿身体接触等因素，与识别包含性骚扰、虐待和暴力的场景存在直接关联。我们还研究并概述了Google Cloud Vision API和Clarifai API等现行最先进的显式内容检测工具为何无法识别和分类这些图像。基于这些定义和特征，我们从各类印度电影场景中构建了首个此类数据集。这些场景被分类为性骚扰、性虐待或性暴力，并以PASCAL VOC 1.1格式导出。我们的数据集已基于识别出的相关特征进行标注，可用于开发和训练深度学习计算机视觉模型以识别这些问题。该数据集已公开供研究开发使用。