A deep-learning approach to early identification of suggested sexual
  harassment from videos
Shreya Shetye Anwita Maiti Tannistha Maiti Tarry Singh
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.