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

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.

Authors

Publication Details

Journal
International Journal of Applied Mathematics Sciences and Technology for National Defense
Published
2025-04-25
DOI
https://doi.org/10.58524/app.sci.def.v3i1.640
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

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

K. Muthumanickam, T. Kathirvel, M R I F F I N, Harish Vishnu K
2 links
International Journal of Applied Mathematics Sciences and Technology for National Defense
Adversarial Robustness in Machine Learning
article

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

K. Muthumanickam, T. Kathirvel, M R I F F I N, Harish Vishnu K
article en

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.

International Journal of Applied Mathematics Sciences and Technology for National DefenseVol. 3(1)
Openalex Percentile: Top 96%
Adversarial Robustness in Machine Learning
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Enhancing digital resilience through GEN-AI driven video content moderation and copyright protection — K. Muthumanickam, T. Kathirvel, et al. · International Journal of Applied Mathematics Sciences and Technology for National Defense (2025) | TGRS Research Map | TGRS