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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67496
Volume:
10 |
9 |
Sep 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Information Technology
D.G Ruparel College
Information Technology
D.G Ruparel College
The rapid advancement of Artificial Intelligence (AI) and generative AI technologies has increased the creation of realistic text, images, audio, and video, making AI-generated misinformation and deepfake content a growing concern on social media. Traditional detection methods often focus on a single type of media and face challenges when dealing with multimodal content, evolving generation techniques, and real-world social media conditions. This study investigates AI-based approaches for detecting manipulated and AI-generated content using Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), computer vision, and multimodal detection techniques. The proposed methodology includes dataset selection, data preprocessing, model implementation, and performance evaluation using standard metrics such as accuracy, precision, recall, F1-score, and Receiver Operating Characteristic–Area Under the Curve (ROC-AUC). The study also examines existing research gaps, including cross-dataset generalization, feature fusion, explainability, and robustness against emerging AI-generation methods. By evaluating suitable detection approaches across different forms of digital content, the research aims to contribute to more reliable automated content verification systems that can improve information integrity and support safer social media environments.
KEYWORDS
This study focuses on investigating Artificial Intelligence-based techniques for detecting AI-generated misinformation and deepfake content in social media environments. By examining existing detection methods, identifying research gaps, and evaluating suitable AI models using standard performance metrics, the research aims to contribute to the development of more reliable automated content verification systems. The findings are expected to support future improvements in digital media security and help address the evolving challenges posed by generative AI technologies.
[1] Mirsky, Y., Lee, W.: The Creation and Detection of Deepfakes: A Survey. ACM Computing Surveys 54(1) (2021) 1–41. [2] Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations 19(1) (2017) 22–36. [3] Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Nießner, M.: FaceForensics++: Learning to Detect Manipulated Facial Images. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2019) 1–11. [4] Dolhansky, B., Howes, R., Pflaum, B., Baram, N., Canton Ferrer, C.: The DeepFake Detection Challenge Dataset. arXiv preprint arXiv:2006.07397 (2020). [5] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Proceedings of NAACL-HLT (2019) 4171–4186. [6] Brown, T., Mann, B., Ryder, N., et al.: Language Models are Few-Shot Learners. In: Advances in Neural Information Processing Systems (NeurIPS) 33 (2020) 1877–1901. [7] Agarwal, S., Farid, H., Gu, Y., He, M., Nagano, K., Li, H.: Protecting World Leaders Against Deep Fakes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2019). [8] Westerlund, M.: The Emergence of Deepfake Technology: A Review. Technology Innovation Management Review 9(11) (2019) 39–52. [9] Vaccari, C., Chadwick, A.: Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception and Uncertainty. Social Media + Society 6(1) (2020).
Publication Date
9/28/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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