2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 17 , Issue 2 , PP: 436-457, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Dalal Thair Mahjoub 1 * , Hala Bahjat Abdulwahab 2
Doi: https://doi.org/10.54216/JISIoT.170229
With the rapid increase of digital content distribution, video watermarking ownership has become an essential tool for detecting certification and tampering. This paper proposes a novel 3D video Zero-Watermarking Framework that integrates machine learning, cryptographic protocol, and entropy-based keyframe selection to ensure strength, inconvenience, and safety. The method operates at two levels: client-side watermark generation and server-side certification. On the client side, the keyframe is extracted using entropy analysis, features are obtained with different 3D Convolutional Neural Network (S3D-CNN), and adaptive noise is generated through the generative adversarial network (GANS). These components are paired with XOR to create a binary watermark key, which undergoes NIST random tests before being safely sent with the original video. On the server, Feige-Fiat-Shamir (FFS) certifies the watermark without highlighting the sensitive information of the zero-knowledge protocol. The system is evaluated against general attacks such as Gaussian noise, JPEG compression, staining, salt-and-pepper, rotation, and scaling. Performance metrics (PSNR, SSIM, NCC, and BER) with FFS protocols, showing 98.7% accuracy in verifying watermark integrity, display strong strength and inevitability. Experimental results, supporting safe and decentralized certification, confirm the effectiveness of the framework proposed to maintain watermarks under various attacks. Future work will focus on integrating blockchain technology and increasing the GAN model for real-world deployment.
Zero-watermarking , Generative convolutional network (GCN) , Feige-Fiat-Shamir (FFS) protocol , 3D video , Zero-knowledge proof , Separable 3D Convolution Network
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