Child Abuse Risk Prediction and Prevention Framework using AI and Dark Web

Abstract

Child abuse is one of the most heinous crimes ever committed in our society. Child sexual abuse (CSA) has recently been widely accepted as a problem in India. About one case every 12 minutes, and 5 children die from child sexual abuse every day. Any form of child abuse or violence is important and will not be ignored. It profoundly affects a child’s mental health so much that it influences later life. Before that, the prevention of child sexual abuse has become a critical issue and a concerted effort in all areas of society: family care, schooling, community-based management, and social norms. Therefore, taking appropriate measures to save every child from violence is essential. A learning strategy for new sexuality against child sexual abuse is proposed to reduce youth violence on the black web. This project proposes a modified LSTM algorithm based on deep learning used to find the purpose of sex and prevent child abuse by not allowing the child to visit the place or person. This CAP API will be able to identify and report child abuse in real-time without violating any privacy. Risk guessing is based solely on web browsers for users. The analysis of child abuse is based on a telemetry database provided by a large Network Service Provider. This database is processed, and the features and patterns hidden in the data are retrieved; finally, LSTM is designed to match the problem of predicting the Sexual Outcome of the selected geo location or mobile using all the information obtained in the analysis step number provided by using SI predicter.CAP is committed to preventing child sexual offences on the black web and artificial intelligence.