## 4 Implementation of the Proposed Method  
In this section, we will present an example of Hermitian positive definite matrix application in steganography. The purpose of our previous paper [8] is to analyze the possible application of the weighted Moore-Penrose inverse and Hermitian positive definite matrix in data protection. This steganographic method consists of splitting a color image into its R, G, and B channels, and after that, a secret message is embedded in the blue layer of carrier color images. The secret message is first translated into a binary record, after which bit replacement is performed with the least weight of those bytes in the carrier image determined by the values of the Hermitian positive definite matrix. Embedded data module  
This module includes an embedded secret message to a carrier, image, or text and is based on the following phases:  
1. Conversion: In this stage, the process begins by loading either a text or an RGB image to serve as the cover image. The Red, Green, and Blue components of the cover image are then separated. Next, the carrier data (Blue component), the Hermitian positive definite (HPD) matrix, and the secret message are loaded. Both the carrier data and secret messages are converted into binary format. If the input is an image, it must first be transformed into a Base64 string before converting to binary. 2. Selection: During this phase, the unique absolute values from the elements of the HPD matrix are selected. The order of the square Hermitian positive matrix should correspond to the length of the message, specifically focusing on the upper triangular portion due to the repeated elements. The number of elements chosen from this upper triangular HPD matrix is based on the length of the secret message. 3. Modification: In this stage, bits from the binary representation of the Blue component of the cover image or text are replaced with bits from the secret message. This is performed by selecting bytes in the cover image according to the values from the upper triangular square HPD matrix and altering their least significant bits (LSBs) to match those of the secret message. The selection of these bytes is guided by the values determined in the previous selection phase. Figure 1 presents a general scenario for embedded secret data, which is based on the weighted Moore-Penrose inverse and Hermitian positive definite upper triangular square matrix. The universal scenario is given by way of an example of an embedded message in some carrier data. **Example 1**. : _Let the cover image, i.e., carrier data, be defined as the following images, Figure 2, and let the defined secret message be \"GET\". Following three phases, we are going to explain how to incorporate a secret message into a cover image._  
A phase of the converting:  
* Carrier data are given as the following image in PNG format  
We are now performing the transformation carried image on three R, G, and B channels (see Figure 3). Figure 1: Universal scenario for data embedding. Figure 2: Carrier image. In this phase, we defined a key in the following way. First, we count the bits of the hidden message, in our case, GET. In binary form, its notation is given as 010001110100010101010100. So, the GET message is represented in binary form with 24 bits. Now, it is necessary to take 24 unique absolute values elements of the upper triangular square Hermitian positive definite matrix, given in the previous phase. These are the following values:  
\[\{339,\,-87,\,-110,\,119,\,9,\,-41,\,-20,\,10\},\]  
\[\{-87,\,514,\,-119,\,10,\,48,\,-55,\,-360,\,45\},\]  
\[\{-110,\,-119,\,395,\,-225,\,-30,\,81,\,-16,\,-129\}\]  
A phase of the change  
In this phase, the characters in the B channel are selected based on the positions in the upper triangular square Hermitian positive definite matrix, and then the Least Significant Bit (LSB) of those characters is replaced. Let us mention that with the replacement, we go from the end of the binary notation. Let us look at an example.