Published on: April 2026
BLOCKCHAIN–AI HYBRID FRAMEWORK FOR PREVENTING COPYRIGHT FRAUD: A NOVEL APPROACH FOR SECURE DIGITAL CONTENT PROTECTION
Gowher Shafi Abhinav Kumar Satyam Kumar Pitamber Kumar Mahto Raj Mishra Kartikeya Pandey Shubham Singh
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Abstract
The rapid growth of digital content has significantly raised copyright abuses, such as unauthorized copying, transmis- sion of ownership, and distribution. Most traditional copyright protection mechanisms rely on a centralized database that can easily be circumvented, altered, or wiped, ultimately complicating ownership history disputes. This paper presents a Blockchain and AI hybrid model that provides immutable copyright registration and automatic fraud detection through content fingerprinting. On one side, Blockchain offers a decentralized, immutable ownership repository, whereas, on the other side, Artificial Intelligence tackles the task of identifying copied, modified, or plagiarized work through deep learning and pattern matching methods. Experimental results demonstrate that combining blockchain immutability with AI-driven recognition significantly mitigates fraudulent claims, strengthens creator trust, and improves trans- parency and enables a more efficient digital rights management (DRM) process [14], [22]. This work further clarifies how the proposed system is dissimilar to the available systems, thus making it a necessary evolution in digital copyright protection.
How to Cite this Paper
Shafi, G., Kumar, A., Kumar, S., Mahto, P. K., Mishra, R., Pandey, K. & Singh, S. (2026). Blockchain–AI Hybrid Framework for Preventing right Fraud: A Novel Approach for Secure Digital Content Protection. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.618
Shafi, Gowher, et al.. "Blockchain–AI Hybrid Framework for Preventing right Fraud: A Novel Approach for Secure Digital Content Protection." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.618.
Shafi, Gowher,Abhinav Kumar,Satyam Kumar,Pitamber Mahto,Raj Mishra,Kartikeya Pandey, and Shubham Singh. "Blockchain–AI Hybrid Framework for Preventing right Fraud: A Novel Approach for Secure Digital Content Protection." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.618.
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References
- Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” 2008.
- Liu et al., “Blockchain-Based Copyright Protection,”
- IEEE Access, 2021.
- He et al., “Deep Residual Learning for Image Recogni- tion,” in Proc. CVPR, 2016.
- Devlin et al., “BERT: Pre-training of Deep Bidirectional Transformers,” in Proc. NAACL, 2019.
- A. Ferrag and L. Maglaras, “Deep Learning for Cy- bersecurity: A Survey,” IEEE Communications Surveys & Tutorials, 2020.
- Yi, R. Paulet, and E. Bertino, “Privacy-Preserving Content-Based Image Retrieval in the Cloud,” IEEE Trans. Inf. Forensics Security, vol. 11, no. 12, pp. 2790–2803, 2016.
- Zhang and J. Liu, “Blockchain-Based Data Provenance for Multimedia Protection,” IEEE Trans. Multimedia, 2020.
- Tiwari and S. Bhatia, “A Comprehensive Survey on Plagiarism Detection,” IEEE Access, vol. 9, pp. 91699– 91716, 2021.
- Mirsky et al., “Deepfake Detection: State-of-the-Art, Open Challenges, and Future Directions,” IEEE Trans. Pat- tern Anal. Mach. Intell., 2021.
- H. Miraz and M. Ali, “Applications of Blockchain Technology Beyond Cryptocurrency,” Annals of Emerging Technologies in Computing, 2018.
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- •Published on: Apr 24 2026
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