ReACT_OCRS: An AI-Driven Anonymous Online Reporting System Using Synergized Reasoning and Acting in Language Models

Abstract

Victims and witnesses of cybercrime often hesitate to report incidents due to concerns over privacy, complexity, and fear of retaliation. Traditional reporting mechanisms require manual data entry, creating accessibility barriers and delaying response times. To address these challenges, this paper introduces ReACT_OCRS, an AI-driven voice-based cybercrime reporting system that allows victims and witnesses to anonymously submit complaints through audio recordings. Leveraging speech recognition transformers, a recent language model, and encryption, the system processes real-time multilingual voice inputs, extracts meaningful content, and classifies reports with high precision using a hybrid voting mechanism. Experimental evaluations on synthetically generated and human-validated datasets confirm the system’s ability to accurately transcribe, classify, and securely process audio complaints while preserving user anonymity. This work improves cybercrime reporting by making it more accessible, efficient, and secure, fostering greater participation from victims and witnesses.

Keywords

• Cybercrime
• encryption
• LLM
• ML
• NLP
• ReACT