Abstract The frequency of digital data transmission or sharing online has heavily increased due to the number of people regularly accessing the internet. This guide explores how easily copyright can be breached on a large scale. Digital multimedia in the form of images, text, audio and video can easily be forged, faked and then shared for profit. To address this issue, digital watermarking has been introduced as a possible solution. In this guide, an optimized blind image watermarking approach based on principal component analysis (PCA) in redundant discrete wavelet (R-DW) domain is proposed. In addition, several attacks are applied to a watermarked image by extracting the message from the attacked image to show the robustness of the proposed PCA-R-DW scheme. Furthermore, an improved grey-wolf optimizer (IGWO) algorithm which has emerged as an efficient meta-heuristic optimization technique is utilized to optimize the performance of the proposed PCA-R-DW blind watermarking (BW) approach. Extensive comparison of test results with conventional BW approaches show the robustness of the proposed framework in terms of quality metrics such as normalized correlation coefficient (NCC), peak signal-to-noise ratio (PSNR), mean square error (MSE) and structural similarity (SSIM) index.

中文翻译：

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一种基于冗余离散小波域主成分分析的优化盲水印方案

摘要 由于定期访问互联网的人数增加，数字数据在线传输或共享的频率大大增加。本指南探讨了大规模侵犯版权的难易程度。图像、文本、音频和视频形式的数字多媒体很容易被伪造、伪造，然后共享以获取利润。为了解决这个问题，引入了数字水印作为可能的解决方案。在本指南中，提出了一种基于冗余离散小波 (R-DW) 域主成分分析 (PCA) 的优化盲图像水印方法。此外，通过从被攻击的图像中提取消息，将几种攻击应用于带水印的图像，以显示所提出的 PCA-R-DW 方案的鲁棒性。此外，改进的灰狼优化器 (IGWO) 算法已成为一种有效的元启发式优化技术，用于优化所提出的 PCA-R-DW 盲水印 (BW) 方法的性能。测试结果与传统 BW 方法的广泛比较显示了所提出的框架在质量指标方面的稳健性，例如归一化相关系数 (NCC)、峰值信噪比 (PSNR)、均方误差 (MSE) 和结构相似性(SSIM) 指数。