Enhancing digital resilience through GEN-AI driven video content moderation and copyright protection
Year: 2025
Type: article
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.
Authors: Muthumanickam K, Kathirvel T, Harish Vishnu K, Mukesh Rajan N
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410012308