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@article{181883,
        author = {Mohammed Abbad Mohiuddin and Dr. Md. Ateeq Ur Rahman and Dr. K.M Subramanian},
        title = {Combating Audio Deepfakes: A Temporal Analysis Using LSTM and RNN Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {111-114},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181883},
        abstract = {Audio deepfakes are burgeoning rapidly with the advancement in AI, where synthetic voices produce the closest approximation of a human voice. Rapid advance of AI creates major threats of misinformation, fraud, and a diminution in trust related to digital communications.sIn this paper, we will propose a new detection framework where we'll use state-of-the-art architectures for neural networks, including CNN and RNN, to analyze audio features for the purpose of identifying synthetic manipulations. We will then test our approach in extensive experiments that validate its effectiveness by showing high detection accuracy and superior performance compared with state-of-the-art approaches.},
        keywords = {CNN, RNN, LSTM, GPU},
        month = {July},
        }