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The aim is to develop a hybrid model to effectively detect audio deepfakes amidst various challenges.

Audio Deepfake Detection Using a Hybrid Model of Convolutional and Bidirectional Long Short-term Memory Networks

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The aim is to develop a hybrid model to effectively detect audio deepfakes amidst various challenges.
Hybrid model combines CNNs and BiLSTM networks for deepfake detection.
Utilized the Release-in-the-Wild dataset for training and evaluation.
Extracted Mel-Frequency Cepstral Coefficients with derivatives for audio preprocessing.
Achieved 99% accuracy in audio deepfake detection.
Obtained an Equal Error Rate of 0.011.
Demonstrated robustness against noise and variability in speaker characteristics.

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Demonstrates a hybrid model for detecting audio deepfakes, suggesting robust performance in real-world conditions.

Al-Halabi et al. (2026) studied this question.

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