使用灰狼优化、选择性加密和快速灵活去噪卷积神经网络的鲁棒双水印技术

Sambhaji Marutirao Shedole, Santhi V
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引用次数: 0

摘要

随着工业互联网技术的发展,物联网系统中的数字数据交换也在蓬勃发展。特别是,越来越多的智能和工业设备创建的数字图像在发送时会涉及到网站、服务器和云的安全问题。为了解决这一问题,本文研究提出了一种安全水印方法,以有效提高安全性、隐蔽性和弹性。首先,利用离散小波变换(DWT)、海森伯分解(HD)和张量-奇异值分解(T-SVD)等各种变换域技术确定信息嵌入的适当系数。利用灰狼优化(GWO)方法,我们估算了适当的嵌入因子,以便在鲁棒性和隐蔽性之间取得合理的折衷。为了使建议的方法提供更高的安全性,我们在水印图像上使用了选择性加密技术。此外,FFDNet--一种快速、适应性强的去噪卷积神经网络--正在发挥作用,以提高建议算法的鲁棒性。结果表明,所推荐的水印方法具有卓越的不可感知性、复原性和安全性,可抵御标准攻击。此外,比较结果表明,建议的算法比其他策略性能更好。达到的指标如下峰值信噪比 (PSNR)、结构相似性指数 (SSIM)、每秒变化像素数 (NPCR) 和统一平均变化强度 (UACI) 平均得分分别为 51.6966 dB、0.9944、0.9961 和 0.2849。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Robust Dual Watermarking using Grey Wolf Optimization, Selective Encryption and Fast Flexible De-Noising Convolution 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 optimization (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.
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