Blind Watermarking Scheme for Un-Compressed Video Using RBF Neural Network
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2013, Vol 2, Issue 5
Abstract
Robust watermarking is one of the major multimedia processing applications which is specifically important for compressed and uncompressed video for copyright protection, content authentication and broadcasting. In this paper, we present a robust and blind watermarking scheme for RGB Uncompressed Video in AVI format in DWT domain. The quantized LL3 sub-band coefficients constitute the dataset to train the Radial Basis Function Neural Network (RBFNN). The output of the RBFNN is used to embed a binary watermark in the Y channel of the colored video frames using a pre-specified formula. The watermarked video exhibits good visual quality. The extracted watermarks yield high normalized correlation values which indicate a successful watermark recovery. We also examine robustness by executing three attacks – Gaussian Noise, Gaussian Blur and Scaling. Normalized Cross Correlation values for these attacks are high thereby indicating successful extraction and good robustness. We conclude that the fast watermarking scheme presented in this paper is suitable for real time applications.
Authors and Affiliations
Anurag Mishra, Aruna Jain
Keywords
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