ENHANCEMENT OF WATERMARKING BASED ON DEEP NEURAL NETWORK FOR IMAGES

Abstract

The spread of the Internet of Things applications and the shared massive amount of data leads to the absence of data security and missing product protection, such as copyrights, pharmaceutical products, and online medical result fraud. To address the security gap, it is possible to propose a model based on developing a new filter derived from Laguerre polynomials based on essential mathematical operations by using the parent function to construct a new discrete Laguerre wavelets transform (DLWT) in image analysis with the watermark, as well as reaching an ideal algorithm through the readings from the deep learning convolutional neural network construction. New wavelets are presented, in the resultant network. The technique of separating the host image from the watermark is compared before and after separation is known as the convolution neural network wavelet transform (CNNWT). Peak signal-to-noise ratio (PSNR) and natural correlation coefficient (NCC) values are the most crucial metrics from these results that demonstrate the effectiveness of the suggested algorithm CNNWT with the QR code and the MATLAB is used in this approach. In this work, the watermark is immersed in the host image with the new discrete wavelets and the convolutional neural network, then the attacks are directed to complete the logistical process of recourse the results of PSNR and the values of the Natural Correlation Coefficient (NCC), reaching NC equal to one and worthy PSNR values. The results obtained demonstrate the effectiveness of the suggested theory in this study the quality of the image and the watermark are preserved, which proves the efficiency of the proposed theory in addition, the accuracy is reached 98.64 % with a time of 23 seconds in an epoch.