Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{184271,
        author = {Kshithij Sangam and Bhavisha and Shameeksha and Spoorthi},
        title = {AI-Powered Detection of Deepfakes and Misinformation on Social Media},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {703-709},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184271},
        abstract = {The rapid rise of AI has enabled the creation of realistic deepfakes and the widespread circulation of false information on social media. While these trends raise concerns around privacy, security, and public trust, AI also provides a path toward automated detection and mitigation. The study looks at different ways to spot manipulated images, altered videos, and misleading text by applying techniques from deep learning, NLP, and computer vision. It also reviews the datasets and detection models used, evaluates how well they perform, and highlights both the practical difficulties and ethical questions that should guide future work.},
        keywords = {Artificial intelligence, Deepfake Detection, Misin- formation, Natural Language Processing, Social Media Analysis, Computer Vision},
        month = {September},
        }