Abstract: This research proposes a novel video watermarking model for video frames based on Rotation Scaling Transformation (RST). Most of the available watermarking methods utilize image processing techniques to execute the watermarking task. This proposal adopts a K Dependency Bayesian (KDB) Model for the approximation of the frames according to probability values. In the study presented, video frames are segmented using the Markov Random Field (MRF) segmentation technique where KDB represents every part of the frame region. Singular Value Decomposition (SVD) is used for encryption of the selected feature points. The encrypted areas located at the feature points are utilized for watermark embedding and extraction. During the stage of embedding, the text files are converted into a matrix. Both the matrices are then merged using the sum of the matrices. The watermark embedding strength is adapted according to the noise visibility function and the analysis of the probability of error is performed mathematically. Simultaneously, the watermark embedding and extraction techniques are assessed based on a proven mathematical model. Experimental results prove that the proposed MRF-SVD video watermarking method performs better in terms of invisibility and robust behavior in comparison to the previous methodologies under potential attacks such as cropping, rotation, scaling, sharpening and Gaussian noise in case of videos.
S. Poongodi and B. Kalaavathi, 2016. Attack Resistant Analysis Using Secure Singular Value Decomposition and Markov Random Field Based on Rotation Scaling Transformation Invariant Image Watermarking for Video Frames. Asian Journal of Information Technology, 15: 3182-3195.