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@article{169104,
        author = {Pranali Hansaraj Dahake and Prof.Rahul Bhandekar and Nikita Satish Madke and Kajal Radheshyam Patle and Chanakya Shivshankar Dahake},
        title = {Review on Deep Fake video detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {303-308},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169104},
        abstract = {This paper addresses the challenges posed by deepfake technology and reviews four detection methods: deep learning, classical machine learning, statistical analysis, and blockchain solutions. Deep learning techniques, particularly those using convolutional and recurrent neural networks, show greater accuracy than traditional methods. Classical machine learning often struggles with complex manipulations, while statistical approaches depend on data quality. Blockchain offers innovative verification for video authenticity. We introduce the DFN (Deep Fake Network), combining MobileNet components and XGBoost for classification, demonstrating improved accuracy over existing models. Ultimately, we advocate for a hybrid strategy to enhance deepfake detection effectiveness.},
        keywords = {Deepfake Detection, Deep Learning, Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Classical Machine Learning, Feature Extraction, Statistical Analysis, Anomaly Detection, Blockchain Technology, Video Authenticity, Misinformation, Data Verification, Hybrid Framework, Video Content Analysis.},
        month = {November},
        }