Violence Detection on Women using Generative AI
Detection, prevention, and intervention strategies that rely on the application of state-of-the-art technology are required in that violence against women continues to threaten people from all walks of life. Through the applications of OpenAI's GPT models coupled with FastAPI for real-time deployment, this work comes to present an innovative solution in recognizing incidences of violence through Generative Artificial Intelligence. Through GPT's natural language capabilities, this system scans digital interactions for abusive language, harassment, and threats through social media and messaging platforms. Fast application programming interface (Fast API) is used by the system to give it scalability, real-time processing, and effective handling of concurrent requests so that it does not take too long in high-pressure situations. The technology is monitored and reports abusive behaviors through real-time alarm signals to the proper authorities or support services. Since it was trained on a wide variety of datasets, the model can both understand overt and subtle kinds of violence and thus be precise in identifying gender-based abuse that encompasses not only verbal manipulations but also psychological manipulations. This paper aims to deal with the ethical issues, problems, and possible impact of using a combination of GPT and FastAPI in relation to violence detection. The paper therefore underlines an integrated effort from AI developers, the legal frameworks, and social services to provide full mechanisms for protection.
Authors
- Paresh Tanna
- Nikhat Raza Khan (ORCID: https://orcid.org/0000-0003-2377-7560)
- Amit Lathigara (ORCID: https://orcid.org/0000-0003-0409-6487)
- Aayush Shrivastava (ORCID: https://orcid.org/0000-0002-2723-489X)
Institutions
- RK University (IN)
Publication Details
- Published
- 2024-12-06
- DOI
- https://doi.org/10.1109/ihcsp63227.2024.10960165
- Primary Topic
- IoT and GPS-based Vehicle Safety Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00