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Novel Neural Network Based CT‑NSCT Watermarking Framework Based upon Kurtosis Coefficients

机译：基于新型神经网络的基于神经网络的基于CT-NSCT水印框架，基于Kurtosis系数

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摘要

In the research presented here, the novel neural network based watermarking framework is investigated in the area of transformation, while contourlet transform and nonsubsampled contourlet transform are realized to address the proposed idea via the Kurtosis to choose the band of suitable coefficients. It is to note that there are a number of techniques to deal with the aforementioned watermarking framework through the new integration of contourlet transform and nonsubsampled contourlet transform in connection with the perceptron neural network to extract the logo information, appropriately. There is the optimization technique through the genetic algorithm to provide the optimum results in the procedure of designing, as well. The approaches of the embedding and the de-embedding in case of learning algorithm of the neural network via individual training data set are considered in the present research to carry out a series of experiments with different scenario for the purpose of verifying the proposed techniques, obviously.
机译：在此处提出的研究中，在转换领域研究了新型神经网络的水印框架，而Contourlet变换和非求采样的轮廓变换被实现为通过峰度解决所提出的想法，以选择合适的系数的频带。值得注意的是，通过Contourlet变换和非资金上的Contourlet变换结合与Perceptron神经网络的新集成来处理上述水印框架的许多技术，以适当地提取徽标信息。通过遗传算法存在优化技术，可以在设计过程中提供最佳结果。在本研究中考虑了通过各个训练数据集的神经网络学习算法的嵌入和解除嵌入的方法，以便进行一系列具有不同场景的实验，以验证所提出的技术，显然是验证所提出的技术。

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