WSMN: An optimized multipurpose blind watermarking in Shearlet domain using MLP and NSGA-II
This work addresses the need for combined copyright protection and content authentication in multimedia networks, offering an incremental improvement over existing methods.
The paper tackles the problem of digital watermarking for image security by proposing an optimized multipurpose blind watermarking scheme in the Shearlet domain using MLP and NSGA-II, achieving an average PSNR of 38 dB and SSIM of 0.95 for watermarked images while maintaining robustness against attacks.
Digital watermarking is a remarkable issue in the field of information security to avoid the misuse of images in multimedia networks. Although access to unauthorized persons can be prevented through cryptography, it cannot be simultaneously used for copyright protection or content authentication with the preservation of image integrity. Hence, this paper presents an optimized multipurpose blind watermarking in Shearlet domain with the help of smart algorithms including MLP and NSGA-II. In this method, four copies of the robust copyright logo are embedded in the approximate coefficients of Shearlet by using an effective quantization technique. Furthermore, an embedded random sequence as a semi-fragile authentication mark is effectively extracted from details by the neural network. Due to performing an effective optimization algorithm for selecting optimum embedding thresholds, and also distinguishing the texture of blocks, the imperceptibility and robustness have been preserved. The experimental results reveal the superiority of the scheme with regard to the quality of watermarked images and robustness against hybrid attacks over other state-of-the-art schemes. The average PSNR and SSIM of the dual watermarked images are 38 dB and 0.95, respectively; Besides, it can effectively extract the copyright logo and locates forgery regions under severe attacks with satisfactory accuracy.