An adaptive HVS based video watermarking scheme for multiple watermarks using BAM neural networks and fuzzy inference system

Abstract

An efficient reversible adaptive video watermarking scheme for multiple watermarks based on Bi-directional Associative Memory (BAM) Neural Networks and Fuzzy Inference System namely, Multi-BAM-FUZ scheme is proposed in this paper. The main goal of this paper is to design a robust video watermarking system which facilitates secure video transmission over a communication channel by maintaining a trade-off among imperceptibility, robustness and watermark capacity or payload. The BAM neural network supports creation of weight matrix (formed out of multiple images) and this matrix is embedded into the DWT uncorrelated mid frequency coefficients of all the components (Y, Cb, Cr) of every frames of the video with varying embedding strength ‘α’. This adaptive embedding strength is generated using the Fuzzy Inference System which takes HVS characteristics such as luminance, texture and edge of each frame as an input in the DWT transform. The simulations performed on various test videos demonstrate that the proposed Multi-BAM-FUZ not only outperforms other existing methods with respect to various video degradation processes, but also maintains a satisfactory image quality, robustness and payload. It is noted that, the implementation of the novel adaptive process enhances the visual quality of about 60.97dB in terms of PSNR and 0.9998 in terms of SSIM, robustness of about nearly 1.0000 and 0.9999 in terms of Normalized Cross Correlation (NCC) value and Bit Correction Rate (BCR) respectively against various attacks. Moreover, the proposed scheme facilitates high level of payload without affecting the imperceptibility and robustness level.