An Optimized Hybrid Algorithm for Blind Watermarking Scheme Using Singular Value Decomposition in RDWT-DCT Domain. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Rajani, D.; Kumar, P. Rajesh
• Is Part Of: Journal of applied security research. Volume 17:Issue 1(2022)
• Page Start: 103
• Page End: 122
• Publication Date: 2022-01-02
• Subjects: Blind watermarking -- redundant discrete wavelet transform -- discrete cosine transform -- singular value decomposition -- Porcellio Scaber algorithm; Police, Private -- Training of -- Periodicals; Police, Private -- Training of -- United States -- Periodicals; Private security services -- Periodicals; 363.289
• DOI: 10.1080/19361610.2020.1838251
• Languages: English
• ISSNs: 1936-1610

Abstract

Abstract: Watermarking is a technique which offers more robustness and good imperceptibility. Even if the algorithmic principle is public or the existence of hidden information is known, it is difficult to attacker to destroy the embedded watermark without degrading the watermarked media severely. In existence, non-blind watermarking (NBW) schemes and different combinations of techniques are proposed for watermarking procedure. But these methods failed to provide the good imperceptibility, lack of robustness and embedding capacity standards, respectively. Thus, to overcome this problem, a hybrid algorithm for blind watermarking (BW) is proposed, which utilizes redundant discrete wavelet transform (RDWT), discrete cosine transform (DCT) and singular value decomposition (SVD) together to have the advantages of all three. In addition, optimization of proposed RDWT-DCT-SVD is obtained using a new bio-inspired algorithm called Porcellio Scaber algorithm (PSA). Thus, the visual perception of extracted watermark image also good enough by maintaining the robustness against various image attacks. Experiments are carried out to test the proposed BW algorithm and it is successful under various geometrical and non-geometrical attacks. Finally, the performances of proposed BW using RDWT-DCT-SVD with PSA optimization is compared with various existing BW approaches in terms of various quality metrics like peak signal to noise ratio (PSNR), structural similarity (SSIM) index, root mean square error (RMSE) and normalized correlation coefficient (NCC).