Non-blind RGB watermarking approach using SVD in translation invariant wavelet space with enhanced Grey-wolf optimizer

Sharing or transmitting the digital information in online is increasing day by day since the usage of internet has become a habituation for everyone, which led to the large-scale violation of copyright issues. Now a days, majority of the data is being shared in the form of digital images which is quite easy for the copyright violators to forge and fake those images and then shared for profit. To deal with these copyright violations, digital watermarking came into existence as a potential solution that utilizes the concept of data hiding. This article proposed an approach for a non-blind color image watermarking (NB-CIW) by employing the algorithm named as singular value decomposition in translation invariant wavelet (SVD-TIW) domain. In addition, to further optimize the proposed SVD-TIW algorithm, enhanced grey-wolf optimizer (E-GWO) is presented which is an efficacious optimization approach in meta-heuristic algorithms. Further, to disclose the robustness and effectiveness of proposed NB-CIW using SVD-TIW-EGWO approach, different sort of attacks is enforced on watermarked image and extracted the accurate watermark image. Simulations on various test images with comparison to the state-of-art NB-CIW methodologies demonstrate the superiority of proposed NB-CIW using SVD-TIW-EGWO approach with respect to quality evaluation metrics like normalized cross correlation (NCC), root mean square error (RMSE), structural similarity (SSIM) index and even that of peak signal-to-noise ratio (PSNR) as well.