Enhancing digital resilience through GEN-AI driven video content moderation and copyright protection
DOI:
https://doi.org/10.58524/app.sci.def.v3i1.640Keywords:
Content DB, Digital Signature, Fingerprinting, Computer-VisionAbstract
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.
References
Ahmed, S. H., Hu, S., & Sukthankar, G. (2023). The potential of vision-language models for content moderation of children's videos. Proceedings of the 2023 International Conference on Machine Learning and Applications (ICMLA), 1237-1241. https://doi.org/10.1109/ICMLA58977.2023.00186
Abdali, A. -M. R., & Al-Tuma, R. F. (2019). Robust real-time violence detection in video using CNN and LSTM. Proceedings of the 2019 2nd Scientific Conference of Computer Sciences (SCCS), 104-108. https://doi.org/10.1109/SCCS.2019.8852616
Balat, M., Gabr, M., Bakr, H., & Zaky, A. B. (2024). TikGuard: A deep learning transformer-based solution for detecting unsuitable TikTok content for kids. Proceedings of the 2024 6th Novel Intelligent and Leading Emerging Sciences Conference (NILES), 337-340. https://doi.org/10.1109/NILES63360.2024.10753192
Chaudhari, A., Davda, P., Dand, M., & Dholay, S. (2021). Profanity detection and removal in videos using machine learning. Proceedings of the 2021 6th International Conference on Inventive Computation Technologies (ICICT), 572-576. https://doi.org/10.1109/ICICT50816.2021.9358624
Gadelkarim, M., Khodier, M., & Gomaa, W. (2022). Violence detection and recognition from diverse video sources. Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN), 1-8. https://doi.org/10.1109/IJCNN55064.2022.9892660
Hidayatullah, A. F., Kalinaki, K., Aslam, M. M., Zakari, R. Y., & Shafik, W. (2023). Fine-tuning BERT-based models for negative content identification on Indonesian tweets. Proceedings of the 2023 8th International Conference on Information Technology and Digital Applications (ICITDA), 1-6. https://doi.org/10.1109/ICITDA60835.2023.10427046
Kapse, A. S., Dubey, A., Bisen, H., Kumar, K., & Tamheed, M. (2023). Multilingual toxic comment classifier. Proceedings of the 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), 1223-1228. https://doi.org/10.1109/ICICCS56967.2023.10142540
Kumar, M., Yuvaraj, T., Priya, G. S., & Manikandan, V. M. (2023). Mitigating health risks and ensuring safe video streaming environments through automated video content moderation. Proceedings of the 2023 International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS), 1-6. https://doi.org/10.1109/iQ-CCHESS56596.2023.10391638
Mancino, D., Guidi, B., Michienzi, A., & Viviani, M. (2025). Striking the balance: Evaluating content quality and reward dynamics in blockchain online social media. IEEE Access, 13, 21927-21945. https://doi.org/10.1109/ACCESS.2025.3536205
Niu, Y., Gao, S., Zhang, H., & Gong, Y. (2023). A decentralized quality management scheme for content moderation. Proceedings of the 2023 International Conference on Networking and Network Applications (NaNA), 215-220. https://doi.org/10.1109/NaNA60121.2023.00043
OpenAI CLIP Model: https://openai.com/research/clip
OpenAI Whisper Speech-to-Text Model: https://openai.com/research/whisper
Prudhvish, Nagarajan, G, Kumar, U. B., Vardhan, B. H., & Kumar, L. T. (2024). DeTox: A web app for toxic comment detection and moderation. Proceedings of the 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies, 1-5. https://doi.org/10.1109/TQCEBT59414.2024.10545229
Rishab, K. S., Mayuravarsha, P., Kanchan, Y. S., Pranav, M. R., & Ravish, R. (2023). Detection of violent content in videos using audio-visual features. Proceedings of the 2023 International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), 600-605. https://doi.org/10.1109/ICAECIS58353.2023.10170034
Sasidaran, K., & G, J. (2024). Multimodal hate speech detection using fine-tuned Llama 2 model. Proceedings of the 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS), 1-6. https://doi.org/10.1109/IACIS61494.2024.10722018
Singhal, M., et al. (2023). SoK: Content moderation in social media, from guidelines to enforcement, and research to practice. Proceedings of the 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P), 868-895. https://doi.org/10.1109/EuroSP57164.2023.00056
Tang, T., Wu, Y., Wu, Y., Yu, L., & Li, Y. (2022). VideoModerator: A risk-aware framework for multimodal video moderation in e-commerce. IEEE Transactions on Visualization and Computer Graphics, 28(1), 846-856. https://doi.org/10.1109/TVCG.2021.3114781
Widyadhana, D. P., Adi, P. A. S., Purwitasari, D., & Arifiani, S. (2023). Recommendation system with Faster R-CNN for detecting content violation in broadcasting videos. Proceedings of the 2023 IEEE 7th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 357-362. https://doi.org/10.1109/ICITISEE58992.2023.10405329
Yang, L., Wu, Z., Hong, J., & Long, J. (2023). MCL: A contrastive learning method for multimodal data fusion in violence detection. IEEE Signal Processing Letters, 30, 408-412. https://doi.org/10.1109/LSP.2022.3227818
Yuan, J., Yu, Y., Mittal, G., Hall, M., Sajeev, S., & Chen, M. (2024). Rethinking multimodal content moderation from an asymmetric angle with mixed-modality. Proceedings of the 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 8517-8527. https://doi.org/10.1109/WACV57701.2024.00834
YouTube Content ID: https://developers.google.com/youtube/partner
Zhai, Z. (2022). Rating the severity of toxic comments using BERT-based deep learning method. Proceedings of the 2022 IEEE 5th International Conference on Electronics Technology (ICET), 1283-1288. https://doi.org/10.1109/ICET55676.2022.9825384
Zhao, W., Lin, X., Chen, Y., Hong, Y., & Zheng, W. (2022). A blockchain-based copyright protection system for short videos. Proceedings of the 2022 IEEE International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), 929-936. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00123
Zhao, Z., Palani, H., Liu, T., Evans, L., & Toner, R. (2024). Multimodal guidance network for missing-modality inference in content moderation. Proceedings of the 2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), 1-4. https://doi.org/10.1109/ICMEW63481.2024.10645412
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