Deepfake Voice Detection Using Convolutional Neural Networks: A Comprehensive Approach to Identifying Synthetic Audio
Eshika Jain, Amanveer Singh · 2024
Deepfake voice recognition has lately been one of the critical challenges in the domain of cybersecurity and digital forensics, as it has recently become increasingly used with deep learning models to create absolutely indistinguishable audio imitations. This paper proposes a CNN-based approach to detect and classify deepfake audio clips. The model is trained on an exhaustive dataset that includes both real and deepfake voice records, putting much attention into the difference between natural and manipulated speech patterns. Our approach is based on the spectrogram analysis-a method of audio signal processing by converting it into a visual format that the CNN then analyzes to extract and categorize features. We test the performance of this model, which has gone through rigorous training with an accuracy of 82.6% and a test loss of 0.386. Results indicate that the proposed CNN architecture successfully learns nuanced acoustic variations between real and synthetic voices, setting up a robust framework for the detection of deepfakes. Moreover, the generalizability of the model has been checked multiple times, keeping the training and validation metrics very close. Future works will further improve the detection accuracy by including more pre-processing techniques and increasing the dataset with more varieties of deepfake sources. The study therefore adds to the ever-increasing demand for reliable and capable tools in the identification of audio forgeries, which seriously threatens privacy, security, and integrity of information in our time.