A Novel Deep Learning Technique for Detection of Violent Content in Videos
Keywords:
Content moderation, video classifier, specificity, violent videoAbstract
With a data of 2.5 quintillion bytes worth of data that is being generated daily, regulation of media uploaded on the social media sites such as Facebook, Instagram, and Reddit is a challenge. In addition to social media platforms, there are also private messaging platforms like WhatsApp and Microsoft Teams which are used by private companies for information exchange, team collaborations, and team conversations which must be content regulated. Content moderation is performed by people (moderators) who have to manually classify content into safe and not safe for work. The exposure of human content moderators to harmful and violent-content over the internet makes moderation less desirable. In this study, we suggest and create a Machine Learning Model that can recognise violent video content and classify them as violent and non-violent. Audio and video are the two parameters we use as inputs. The input video along with the audio is initially processed, if the audio is classified as violent, then the video is marked and classified as violent video. The associated video is put into a video classifier where it is further classed as violent or non-violent if the audio classifier deems the audio to be non-violent. Performance indicators like precision, accuracy, sensitivity, and specificity are used to demonstrate the performance of the suggested model.
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