AI-Powered Voice Cloning Detection System

This study presents the development of an AI-powered voice cloning detection system that addresses the growing threat of deepfake audio abuse in digital communications. The proposed system integrates advanced machine learning algorithms with spectro-temporal analysis to provide real-time, scalable detection of synthetic voice content. Unlike traditional audio forensics methods, our approach employs a hybrid CNN-RNN architecture combined with contrastive learning techniques to achieve superior detection accuracy across diverse speakers, accents, and synthesis methods. The system processes audio inputs through comprehensive feature extraction including MFCCs, spectrograms, and speaker embeddings, utilizing a multi-stage pipeline for robust classification. Preliminary analysis indicates that AI-driven voice cloning detection can achieve significantly higher accuracy rates compared to human listeners (>90% vs 60-70%) while maintaining real-time processing capabilities. The research contributes to bridging the gap between traditional audio forensics and modern deepfake-aware security systems through innovative feature fusion and adaptive learning mechanisms. The proposed architecture demonstrates potential for deployment in telecommunications, financial services, and media verification platforms where audio authenticity is critical.