Forensic Analysis of Deepfake Audio Detection
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Abstract
The rise of deepfake audio technologies poses significant challenges to authenticity verification, necessitating effective detection methods. Traditional techniques, such as manual forensic analysis, basic machine learning approaches, speech-to-text conversion, and Short-Time Fourier Transform (STFT) analysis, have been employed to identify manipulated audio. However, these methods often fall short due to their timeconsuming nature, inability to handle complex sequential data, and susceptibility to high-quality synthetic audio. This paper presents an innovative approach that leverages Long Short-Term Memory (LSTM) networks and Mel-Frequency Cepstral Coefficients (MFCC) for deepfake audio detection. By harnessing the power of deep learning, LSTMs can effectively capture temporal dependencies in audio data, allowing for the identification of subtle anomalies that indicate manipulation. The use of MFCC enables the extraction of robust audio features that align closely with human auditory perception, thereby enhancing the model’s sensitivity to synthetic alterations. Additionally, our methodology incorporates enhanced preprocessing techniques to ensure high-quality input data, thereby further improving detection accuracy. The proposed system demonstrates a significant advancement in deepfake audio detection, providing a more reliable solution against increasingly sophisticated audio manipulations.
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