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@article{166938,
        author = {M. ARUN KUMAR and M. MOHAMED RAFI and M.SABARI RAMACHANDRAN},
        title = {DEEP FAKE DETECTION USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {2629-2633},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166938},
        abstract = {Deep fake technology has rapidly evolved, enabling the creation of highly realistic fake videos and images that are increasingly difficult to distinguish from authentic content. The proliferation of deep fakes poses significant challenges to the integrity of digital media and has profound implications for various sectors, including journalism, politics, and cybersecurity. Detecting deep fakes has become a critical area of research and development in the fields of computer vision, and digital forensics. This abstract provides an overview of the current state-of-the-art techniques and challenges in deep fake detection. We discuss the underlying principles of deep fake generation and explore the various methodologies employed to identify and mitigate the spread of manipulated media. Techniques range from traditional forensic analysis to advanced deep learning algorithms trained on vast datasets of both authentic and synthetic media.},
        keywords = {Deep fake technology, Deep learning algorithms},
        month = {July},
        }