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@article{186911,
        author = {PUGAZHMANI T and PRAVEEN RAJ P and MEITHAVAM T and BALASWATHY S},
        title = {DEEPFAKE DETECTION USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {2607-2613},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186911},
        abstract = {Deepfake videos pose a growing threat in misinformation, fraud, and privacy breaches. This research proposes a deepfake detection system using ResNeXt-50 for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal motion pattern detection.  The combined spatio-temporal architecture captures visual anomalies and unnatural facial motion transitions, improving the reliability of deepfake video classification. Experimental results show that the proposed approach enhances detection accuracy, making it suitable for video authenticity verification applications.},
        keywords = {Deepfake Detection, ResNeXt-50, LSTM, Video Forensics, Spatio-Temporal Learning},
        month = {November},
        }