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Enhancing Digital Child Safety Through a Transformer-Based Framework for Abnormal Behavior Detection
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Author(s): Vinod Mahor (Maulana Azad National Institute of Technology, India), Jaytrilok Choudhary (Maulana Azad National Institute of Technology, India)and Dhirendra Pratap Singh (Maulana Azad National Institute of Technology, India)
Copyright: 2026
Pages: 28
Source title:
Integrating Parental Consent and Child Engagement With Digital Protection Rules
Source Author(s)/Editor(s): Romil Rawat (LabGeoInf-Research LABoratory in GEOmatics and INFormation systems, National Research Council in Italy, Rome, Italy), Sanjaya Kumar Sarangi (Utkal University, India), A. Samson Arun Raj (Karunya Institute of Technology and Sciences, India), Janet Olivia Richmond (Karunya Institute of Technology and Sciences, India)and Purvee Bhardwaj (Rabindranath Tagore University, India)
DOI: 10.4018/979-8-3373-2716-7.ch007
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Abstract
The proliferation of digital platforms has significantly increased children's exposure to online threats, necessitating advanced and intelligent behavior monitoring systems. This study proposes a Transformer-based framework for abnormal behavior detection aimed at enhancing digital child safety. contextual relationships within sequential data, enabling superior detection of complex behavioral patterns. Leveraging attention mechanisms, the model integrates spatiotemporal features from video surveillance data to identify potential threats such as cyberbullying, online grooming, and inappropriate interactions. Trained on the ShanghaiTech Campus Dataset, the proposed model achieves a training accuracy of 96.4%, with a precision of 93.5% and recall of 94.5%, The system supports real-time analysis and proactive intervention across digital communication platforms and surveillance environments. This Transformer-based approach presents a promising direction for intelligent, context-aware child safety monitoring in an increasingly digital world
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