## 5 Steganalysis of the Proposed Method  
The hypothesis in this context might be related to the performance, effectiveness, or security of the data-hiding technique using the Hermitian positive definite matrix. Our hypothesis is as follows: \"The data-hiding technique using Hermitian positive definite  
Figure 11: Histogram of the original B channel of the image. Figure 12: Histogram of the B channel of stego-image with a secret massage. Figure 13: The result of comparing histograms of the original and stego-images. matrix provides higher imperceptibility and robustness, compared to traditional steganographic methods\". In experiments, the control group may represent the traditional data-hiding methods or existing steganographic techniques without the use of Hermitian positive definite matrices. Control in this experiment is as follows: \"The control group consists of data-hiding techniques using conventional methods like a least significant bit (LSB) embedding, without the involvement of Hermitian positive definite matrices\". Based on models from our previous research [22; 29], steganalysis was based on the following tests:  
1. The amount of information per pixel in stego-image
2. Approximate Entropy: original vs. stego-image
3. Bit distribution in stego-image  
The amount of information per pixel  
The capacity for hiding information within a pixel using a data-hiding technique based on Hermitian positive definite matrices is influenced by various factors, such as the matrix size and the specific algorithm employed. In steganography, data are typically concealed by making subtle, imperceptible changes to pixel values. Several elements can impact the data-hiding capacity, including matrix size, embedding method, payload size, and perceptual transparency. The size of the Hermitian positive definite matrix can directly affect the volume of information that can be concealed, with larger matrices generally allowing for more data embedding. Additionally, the effectiveness of the algorithm used for data embedding plays a crucial role; some methods may be more adept at hiding data than others. Ultimately, the amount of data intended for concealment in an image will determine the information that can be embedded per pixel (see Figure 14). The payload size is usually measured in bits or bytes. The level of distortion that can be introduced into the image without being perceptible to the human eye is a crucial consideration. Hermitian positive definite matrices are often chosen to minimize visual artifacts, but the amount of distortion that can be introduced without detection varies depending on the image content and quality. Approximate entropy (\(ApEn\)) is a measure used in various fields, including signal processing and information theory, to quantify the regularity or predictability of a time series or sequence of data. In the context of data-hiding techniques using Hermitian positive definite matrices, ApEn can be used to assess the randomness or complexity of the embedded data. When you hide data within a Hermitian positive definite matrix, you are making specific modifications to the matrix elements to encode information. These modifications can be thought of as a sequence of data points. A high \(ApEn\) value suggests that the data embedding process is relatively unpredictable and may be more resistant to detection by statistical analysis. A low ApEn value might indicate a more regular or predictable pattern, which could potentially make the hidden data more vulnerable to detection. It is important to note that the specific calculation of ApEn can vary depending on the application and the way data are embedded using Hermitian positive definite matrices. Also, the choice of matrix size, embedding technique, and other factors can influence the ApEn value (see Figure 15). Figure 14: Input parameters in the process of embedding data. The goal is typically to maximize the irregularity in the embedding process while ensuring that the embedded data remain recoverable.