Adaptive video watermarking integrating a fuzzy wavelet-based human visual system perceptual model

An adaptive Fuzzy Inference Perceptual model has been proposed for watermarking of video streams. The model is designed to be adaptive to the human visual characteristics to attest the owner identification and discourage the unauthorized copying. The proposed model integrates the human visual characteristics of video motion sub-regions, in the frequency multi-resolution wavelet domain, with multi-dimensional fuzzy inference perceptual model. The designed fuzzy multi-variable input system inherits luminance, texture and gradient to generate an adaptive watermark embedding strength factor. Interacting motion estimation is applied for the selection of the candidate frames and sub-blocks. A fuzzy-based scheme has been proposed to generate a perceptual membership degree for strength watermark embedding factor for the selected candidate motion blocks. Experiments have been carried out with different benchmark test videos of different sizes, visual characteristics and sampling rates. Various sizes of watermark signatures have been applied on the model. Several experimental attacks have been applied such as frame dropping, frame averaging, JPEG compression, Gaussian noises. It manifests considerable robustness against various geometric and signal processing attacks. Several attacks have been applied to the proposed scheme and the experiments revealed promising results in terms of visual quality and extracted watermark distortion. In addition, the model has been compared with different other watermarking schemes in literature. The proposed model showed superior performance in terms of fidelity, robustness to attacks and high level of imperceptibility.