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@article{175340,
        author = {Gullipalli Gnana Prasmitha and A.Sravanthi and Kethagani Akhila and Arumbaka Krupa Sujitha and Chandaka Supriya and Imandi Durga Satya Naga Maleswari},
        title = {DEEP FAKE FACE DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3480-3484},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175340},
        abstract = {Deepfake technology has seen significant advancements due to improvements in artificial intelligence and deep learning. While these developments have enabled new applications in media and entertainment, they have also introduced serious ethical and security concerns. This research presents a robust deepfake face detection system that integrates convolutional neural networks (CNNs) within a Flask-based backend. The system utilizes ResNet for preprocessing images, VGG16 for deepfake classification, Clerk for authentication, and Nodemailer for email notifications. We train our model using the FaceForen- sics++ and DeepFake Detection Challenge (DFDC) datasets. The results demonstrate high detection accuracy, highlighting the effectiveness of our approach. The paper also discusses potential real-world applications and future improvements.},
        keywords = {Deepfake detection, Flask, ResNet, VGG16, authentication, cyber security.},
        month = {April},
        }