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@article{205968,
        author = {Anushka Ghadekar and Shruti Jamdade and Harshita Allewar and Soun Aadhe and Prof. S. B. Gawande},
        title = {Automated Deepfake Identification Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {8824-8830},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205968},
        abstract = {The rapid advancement of Artificial Intelligence (AI) and deep learning technologies has led to the widespread creation of deepfakes, which are digitally manipulated images and videos that appear highly realistic. While deepfakes have applications in entertainment and media production, they also pose significant threats to privacy, security, and the authenticity of digital content. The increasing misuse of deepfake technology for spreading misinformation, identity theft, and cybercrime has created a pressing need for reliable detection mechanisms.
This research presents an automated deepfake identification system based on deep learning techniques. The proposed framework utilizes Convolutional Neural Networks (CNNs) to analyze facial features, texture inconsistencies, and manipulation artifacts present in images and video frames. The system performs preprocessing, feature extraction, and classification to distinguish between authentic and manipulated media. By leveraging large-scale deepfake datasets and advanced image analysis methods, the model aims to achieve high detection accuracy while maintaining computational efficiency.},
        keywords = {Deepfake, Machine Learning, Deep Learning, Convolutional Neural Networks (CNN), Digital Media Forensics.},
        month = {June},
        }