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@article{178262,
        author = {Vinay Kumar M S and Vardhan R Gowda and Piyush Kumar and Mrs. T Bhagyalakshmi},
        title = {Deepfake detection using ResNeXt and LSTM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4481-4484},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178262},
        abstract = {With the rapid rise of manipulated media content, particularly deepfakes, the integrity of digital information is increasingly under threat. This paper presents an AI-powered deepfake detection system combining the strengths of ResNeXt convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) networks. Utilizing transfer learning, features are extracted via pretrained ResNeXt models, followed by sequence analysis using LSTM. The system is evaluated on benchmark datasets such as FaceForensics++, Celeb-DF, and the Deepfake Detection Challenge, achieving detection accuracies of 96% and 97.14% on FaceForensics++ and Celeb-DF respectively. This project showcases an effective deep learning approach to tackle the growing challenge of synthetic media.},
        keywords = {Deepfake Detection, Video Forgery, ResNeXt, LSTM, Transfer Learning, FaceForensics++, Celeb-DF, Artificial Intelligence, Temporal Feature Extraction, Forgery Detection.},
        month = {May},
        }