Speaksafe: Deep Feature Analysis for Preventing Voice Cloning and Audio Impersonation Attacks
DOI:
https://doi.org/10.15662/IJEETR.2026.0802413Keywords:
Audio authentication, Convolutional Neural Network, Deepfake audio, Feature extraction, Mel-Frequency Cepstral Coefficients, Voice cloning, Voice impersonationAbstract
The rapid advancement of AI-driven voice synthesis technologies has led to a significant increase in deepfake audio, enabling realistic voice impersonation that poses serious threats to digital security, privacy, and trust. Such manipulated audio can be exploited for fraudulent activities, including financial scams, unauthorized transactions, and bypassing voice-based authentication systems. Traditional detection techniques struggle to identify these sophisticated forgeries due to their reliance on basic signal characteristics and inability to capture subtle distortions embedded in synthetic speech. This research presents an intelligent framework for detecting fake audio by combining Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction with a Convolutional Neural Network (CNN) for classification. MFCC captures essential frequency-domain representations that reflect perceptually relevant speech characteristics, while the CNN model learns complex patterns and anomalies that differentiate real and manipulated audio signals. The proposed approach is trained and evaluated using publicly available datasets, ensuring robustness across diverse speech conditions. The results demonstrate that the framework achieves high accuracy in distinguishing authentic and synthetic audio, offering a scalable and automated solution for audio verification. This research contributes to strengthening cybersecurity measures by enhancing the reliability of voice-based systems and mitigating risks associated with deepfake audio and voice impersonation attacks.
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