Real-Time Regulation of Inappropriate and Defamatory AI-Generated Videos Using Multimodal Deepfake Analysis

  • Unique Paper ID: 194842
  • Volume: 12
  • Issue: 10
  • PageNo: 8087-8094
  • Abstract:
  • Social media platforms increasingly face threats from inappropriate and defamatory AI-generated videos, leading to reputational damage, legal risks, and compromised user safety. This paper presents a secure and scalable AI-based system for real-time detection of harmful AI-generated video content. Developed using a multimodal deepfake analysis model combining visual, audio, and metadata features, the system supports intent-aware classification and pre-upload filtering of flagged content. Designed for platform-level deployment, the model enables content screening with high accuracy, minimal latency, and low false positive rates. The proposed approach addresses limitations of manual or post-upload moderation by enabling proactive detection, reducing harmful content visibility, and supporting ethical governance. Key innovations include transformer-based fusion, contextual intent classification, and deployment-ready architecture for integration into existing platform workflows. Evaluated through simulated upload pipelines and benchmark datasets, the system demonstrates strong reliability and practical feasibility for content governance in high-traffic digital environments. This work contributes a modular and ethical AI framework for securing online media platforms against malicious AI-generated video content.

Copyright & License

Copyright © 2026 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.

BibTeX

@article{194842,
        author = {Dr V. Saipriya and P.K.S.R. Rahul Varma and M. Bhanu and P. Bhavita Naga Devi and V. Tanusha},
        title = {Real-Time Regulation of Inappropriate and Defamatory AI-Generated Videos Using Multimodal Deepfake Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8087-8094},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194842},
        abstract = {Social media platforms increasingly face threats from inappropriate and defamatory AI-generated videos, leading to reputational damage, legal risks, and compromised user safety. This paper presents a secure and scalable AI-based system for real-time detection of harmful AI-generated video content. Developed using a multimodal deepfake analysis model combining visual, audio, and metadata features, the system supports intent-aware classification and pre-upload filtering of flagged content. Designed for platform-level deployment, the model enables content screening with high accuracy, minimal latency, and low false positive rates. The proposed approach addresses limitations of manual or post-upload moderation by enabling proactive detection, reducing harmful content visibility, and supporting ethical governance. Key innovations include transformer-based fusion, contextual intent classification, and deployment-ready architecture for integration into existing platform workflows. Evaluated through simulated upload pipelines and benchmark datasets, the system demonstrates strong reliability and practical feasibility for content governance in high-traffic digital environments. This work contributes a modular and ethical AI framework for securing online media platforms against malicious AI-generated video content.},
        keywords = {Deepfake Detection, AI-Generated Video, Real-Time Content Regulation, Multimodal Analysis, Transformer Models, Ethical AI, Video Upload Screening, Intent Classification, Platform Integration, Social Media Safety},
        month = {March},
        }

Cite This Article

Saipriya, D. V., & Varma, P. R., & Bhanu, M., & Devi, P. B. N., & Tanusha, V. (2026). Real-Time Regulation of Inappropriate and Defamatory AI-Generated Videos Using Multimodal Deepfake Analysis. International Journal of Innovative Research in Technology (IJIRT). https://doi.org/doi.org/10.64643/IJIRTV12I10-194842-459

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