Enhancing digital resilience through GEN-AI driven video content moderation and copyright protection

Authors

  • Muthumanickam K Kongunadu College of Engineering and Technology (Autonomous)
  • Kathirvel T Kongunadu College of Engineering and Technology (Autonomous)
  • Harish Vishnu K Kongunadu College of Engineering and Technology (Autonomous)
  • Mukesh Rajan N Kongunadu College of Engineering and Technology (Autonomous)

DOI:

https://doi.org/10.58524/app.sci.def.v3i1.640

Keywords:

Content DB, Digital Signature, Fingerprinting, Computer-Vision

Abstract

In the digital era, ensuring digital resilience in video content moderation and copyright enforcement is crucial due to the vast volume of uploads. Traditional manual review methods are inefficient, necessitating AI-driven automation. This paper presents an AI-powered system integrating computer vision, deep learning, and NLP for real-time video analysis. The system detects inappropriate content using CLIP for visual moderation and Whisper for speech analysis, ensuring high-precision filtering with human oversight. A copyright protection mechanism employs watermarking and fingerprinting to generate unique digital signatures, preventing unauthorized content usage. A React-based UI with Vite framework provides an interactive reviewer experience. By combining automation with human intervention, this approach enhances moderation accuracy, copyright enforcement, and compliance with global content standards, fostering a more secure and resilient digital ecosystem. This system enhances digital resilience and security, making it applicable for defense and national security in protecting sensitive content.

Author Biographies

  • Muthumanickam K, Kongunadu College of Engineering and Technology (Autonomous)
    Department of Information Technology
  • Kathirvel T, Kongunadu College of Engineering and Technology (Autonomous)
    Department of Information Technology
  • Harish Vishnu K, Kongunadu College of Engineering and Technology (Autonomous)
    Department of Information Technology
  • Mukesh Rajan N, Kongunadu College of Engineering and Technology (Autonomous)
    Department of Information Technology

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Published

2025-04-25

How to Cite

K, M., T, K., K, H. V., & N, M. R. (2025). Enhancing digital resilience through GEN-AI driven video content moderation and copyright protection. International Journal of Applied Mathematics, Sciences, and Technology for National Defense, 3(1), 25-34. https://doi.org/10.58524/app.sci.def.v3i1.640