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DEEP FAKE AUDIO DETECTION USING DEEP LEARNING
The emergence of deep learning-based speech synthesis technologies has enabled the generation of highly realistic artificial audio, commonly known as deep fake audio. These synthetic audio signals can imitate human voices with remarkable accuracy, posing significant threats in areas such as identity theft, misinformation, financial fraud, and digital forensics. Traditional detection methods struggle to identify subtle artifacts present in deep fake audio due to the increasing sophistication of generative models like WaveNet, Tacotron, and GAN-based architectures. This paper proposes an advanced deep learning-based framework for detecting deep fake audio using hybrid neural network architectures. The system integrates Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal sequence modeling. Audio signals are transformed into time-frequency representations such as spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs) to capture discriminative patterns. The model is trained and evaluated on benchmark datasets like ASVspoof, achieving high detection accuracy and robustness across diverse spoofing attacks. Experimental results demonstrate that the proposed approach significantly outperforms traditional machine learning techniques. This research contributes to enhancing audio authentication systems and mitigating risks associated with synthetic media.
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