Ensemble learning model for deepfake audio detection using multi-feature extraction approach

In this paper, several audio features are leveraged to improve detection accuracy through an advanced ensemble learning approach. For the extraction of diverse signal characteristics, the method uses Mel-frequency cepstral coefficients (MFCCs), statistical features, and short-time Fourier transform (STFT) features. Specialized neural networks are utilized to handle each set of features: MLPs categorize speech according to statistical descriptions, RNNs detect temporal anomalies from MFCC, and CNNs examine spectral patterns from STFT. These are finally combined by a meta-learning model through the stacking ensemble method, enhancing robustness and decreasing misclassification. Compared to individual models, the system is more effective when evaluated against the Fake-or-Real dataset. Based on experimental findings, deepfake detection is significantly enhanced and highly accurate when spectral, temporal, and statistical data are fused. In addition, the model is hosted using Gradio on Hugging Face, providing real-time deepfake detection.