## 3 System Model  
This section discusses a novel technique in digital watermarking with data hiding based on segmentation and classification by deep learning techniques. Here, the used input images are Magnetic Resonance Images (MRI) and Computed Tomography (CT) images, which have been processed for noise removal, smoothening and normalization. The processed image has been watermarked using the Singular Value Decomposition-based discrete wavelet transform quantization model. Regarding watermarked data, it was segmented and classified using convolutional generative adversarial neural networks. The proposed architecture is shown in Figure 1.  
Figure 1: Proposed architecture. The first step is to reduce the size of the original image from 512 \(\times\) 512 pixels to 128 \(\times\) 128 pixels, to reduce computation complexity and to assist the network in performing better in less time with simpler calculations. For the system to be trained on unsorted data, as well as to prevent a focus on a certain area of the entire dataset, data were then separated and afterwards shuffled. Data were divided into three sets (training, validation, and test), each one with a separate target label. In order to prevent overfitting and to improve model resilience, images were enhanced with Study I, so the system could recognize them as being brand-new. The photos also received a grayscale distortion, in addition to geometric enhancement. The watermark was inserted into the recurrence area of the picture. Various images might have unique frequency appropriations. The weaknesses of the installed watermark were changed and consequently founded on the recurrence attributes of the image and, basically, an experimentally foreordained planning capability. The watermark-embedding process was executed in the SVD_DWT area in light of the fact that the SVD_DWT can de-create an image into various frequency parts (or different recurrence subbands). Different frequency parts have various aversions to picture pressure, making it much more straightforward to control the watermark weakness. The weakness of a watermark is chiefly impacted by two factors: how many watermark bits are inserted into every frequency part of the image, and the comparing watermark implanting strength, which is constrained by the quantization boundary. The picture quality was assessed in light of the degradation of the extracted watermark. Singular Value Decomposition-Based Discrete Wavelet Transform Quantization Model in Image Watermarking:  
The most widely used matrix factorization technique in recommendation systems is SVD. In order to capture the linear properties of lncRNAs and illnesses, the SVD technique was applied to the proposed model. SVD specifications are as follows:  
In the event that \(R\in Rm\times n\) is an association matrix, then the SVD of matrix \(R\) is the factorization of 3 matrices (\(U\), \(\sum\), and \(V^{T}\)), as shown in Equation (1). \[R=U\sum V^{T} \tag{1}\]  
In Equation (2), the diagonal elements \(\sigma_{\rm i}\) are referred to as the singular values of the matrix \(R\). \[\sigma_{1}\geq\sigma_{2}\geq\cdots\geq\sigma_{n}\geq 0 \tag{2}\]  
By retaining the \(k\) greatest singular values in Equation (3), an approximate representation of the matrix \(R\) can be obtained. \[R\approx U_{k}\sum_{k}(V_{k})^{T} \tag{3}\]  
Output \(y^{(l)}\) (\(j\) = 1, 2, 3,.... \(N\)) can be described as Equation (4), if there are \(L\) hidden nodes and SLFN is a standard one. \[y^{(j)}=\sum_{i=1}^{L}v_{i}g_{i}\Big{(}x^{(j)}\Big{)}=\sum_{i=1}^{L}v_{i}g \Big{(}a_{i}\cdot x^{(j)}+b_{i}\Big{)} \tag{4}\]  
\(\rm a_{i}\) = [\(\rm a_{i1}\), \(\rm a_{i2}\),..., \(\rm a_{i1}\)] The weight vector \(\rm T_{I}\) = 1, 2,..., L) connects the \(\rm i\)th hidden node to all input nodes, and \(\rm v_{i}\) = [\(\rm v_{i1}\), \(\rm v_{i2}\),..., \(\rm v_{im}\)]. The weight vector \(\rm T_{I}\) = 1, 2, 3,..., L) connects the \(\rm i\)th hidden node with all output nodes. Equation (5) provides its general expression.