With the proliferation of deep learning-based voice synthesis technologies, deep fake audio has become an emerging cybersecurity and ethical threat. This project proposes a deep learning-based system for the detection of deep fake audio using advanced neural network architectures. The model leverages spectrogram analysis and convolutional neural networks (CNNs) to learn distinguishing features between authentic and synthesized audio. A dataset consisting of both real and AI-generated audio samples was compiled, preprocessed, and used to train and evaluate the model. Feature extraction techniques, such as Mel-frequency cepstral coefficients (MFCC) and log-mel spectrograms, were employed to convert audio into image-like representations for input into the CNN. Experimental results indicate that the model achieves high classification accuracy in detecting deep fake audio across various speakers and synthetic voice generation techniques. This research demonstrates the potential of deep learning in combating the challenges posed by deep fake technologies. Future work may involve incorporating recurrent neural networks (RNNs) for better temporal feature analysis, as well as expanding the model’s robustness across languages and background noise conditions.
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Deep Fake Audio Detection Using Deep Learning
Semantic Scholar · 2026
Abstract
With the proliferation of deep learning-based voice synthesis technologies, deep fake audio has become an emerging cybersecurity and ethical threat. This project proposes a deep learning-based system for the detection of deep fake audio using advanced neural network architectures. The model leverages spectrogram analysis and convolutional neural networks (CNNs) to learn distinguishing features between authentic and synthesized audio. A dataset consisting of both real and AI-generated audio samples was compiled, preprocessed, and used to train and evaluate the model. Feature extraction techniques, such as Mel-frequency cepstral coefficients (MFCC) and log-mel spectrograms, were employed to convert audio into image-like representations for input into the CNN. Experimental results indicate that the model achieves high classification accuracy in detecting deep fake audio across various speakers and synthetic voice generation techniques. This research demonstrates the potential of deep learning in combating the challenges posed by deep fake technologies. Future work may involve incorporating recurrent neural networks (RNNs) for better temporal feature analysis, as well as expanding the model’s robustness across languages and background noise conditions.