Forensic Investigation of Synthetic Voice Spoofing Detection in Social App

With the rapid growth of social applications, the misuse of synthetic voice generation technologies poses a significant security threat. Voice spoofing, where artificial voices are generated to impersonate real individuals, is a growing concern in various domains, including online communication, authentication, and social media interactions. This paper uses deep learning techniques to present a forensic investigation into the detection of synthetic voice spoofing within social apps. This study integrates a Convolutional Neural Network (CNN) with a Temporal Convolutional Network (TCN) in a hybrid architecture. A lightweight MobileNet CNN first extracts spatial features from Mel-Spectrograms, which are then analyzed by the TCN to capture sequential patterns. Using the fake-or-real (FoR) dataset, the for-norm dataset, this model achieved a training precision of 99.89% and validation accuracy of 99.79% and for-rerec dataset the model achieved a training precision of 99.79% and validation accuracy of 94.22%. Evaluation metrics, including the precision-recall curve with an average precision of 99% and the ROC curve with an AUC of 99%, underscore the model's robustness in distinguishing real from synthetic audio, offering a reliable solution for real-time deployment in resource-constrained environments.