An Optimized Robust Dual Watermarking Using Fast and Flexible De-Noising Convolutional Neural Network

Digital data interchange in IoT systems has flourished with the advancement of industrial internet technologies. Particularly, more and more digital images created by intelligent and industrial equipment are sent there are security concerns related to the website, server, and cloud. To accomplish this issue, in this article a secure watermarking approach is suggested in this research to effectively improve security, invisibility, and resilience at the same time. The adequate coefficient for information embedding is first determined using an assortment of transform domain techniques Discrete-Wavelet-Transform (DWT), Heisenberg- decomposition (HD), and Tensor-singular-value-decomposition (T-SVD). Using the grey wolf optimisation (GWO) approach, we estimated the appropriate embedding factors to provide a reasonable compromise between robustness and invisibility. To enable the suggested approach to offer an additional level of security, a selective encryption technique is used on the watermark image. Moreover, FFDNet—a quick and adaptable de-noising convolutional-neural –network is working to increase the robustness-of-the suggested algorithm. The results demonstrate that the recommended watermarking method generates exceptional imperceptibility, resilience, and security against standard attacks. Additionally, the comparison demonstrates that the suggested algorithm performs better than alternative strategies. The following metrics were reached: 51.6966 dB, 0.9944, 0.9961, and 0.2849 for the peak-signal- to-noise ratio (PSNR), Structural-Similarity-Index (SSIM), number of changing pixels per second (NPCR), and unified-averaged-changed-intensity (UACI) average scores.

随着工业互联网技术的迭代升级，物联网（IoT）系统中的数字数据交互已迎来蓬勃发展。尤为值得关注的是，智能设备与工业装备生成的海量数字图像正被广泛传输，这也使得网站、服务器及云端环节面临诸多安全隐患。为解决该类安全问题，本文提出一种安全水印方案，可同时有效提升水印系统的安全性、不可见性与鲁棒性。本方案首先通过组合多种变换域技术——离散小波变换（DWT）、海森堡分解（HD）与张量奇异值分解（T-SVD）——确定信息嵌入的最优系数；随后利用灰狼优化（GWO）算法求解最优嵌入因子，以在鲁棒性与不可见性之间达成合理平衡。为进一步提升方案的安全防护等级，本文还对水印图像采用了选择性加密技术。此外，本方案引入快速自适应去噪卷积神经网络FFDNet，以进一步增强所提算法的鲁棒性。实验结果表明，所提水印方案在抵御各类常规攻击时，可实现优异的不可见性、鲁棒性与安全性。对比实验同样证实，本算法的综合性能优于其他同类方案。本次实验达成了如下指标：峰值信噪比（PSNR）达51.6966 dB、结构相似性指数（SSIM）达0.9944、像素变化率（NPCR）达0.9961、统一平均变化强度（UACI）达0.2849的平均得分。