## 2 Overview of Related Works  
Due to the rapid development of the Internet, the issue of protecting information transmitted through it, especially sensitive and confidential data, has gained significant attention. Individuals and governmental and non-governmental organizations worldwideare actively researching this matter, leading to various proposed solutions. Among these solutions, cryptographic techniques have gained the most attention. Several renowned encryption methods have been introduced, such as Data Encryption Standard (DES), Advanced Encryption Standard (AES), RSA [14], and Blowfish [15]. Additionally, variations of these methods have been developed to align with practical requirements, such as CS-AES [16], AES modifications for MPEG video data streams [17], AES modifications for image encryption [18; 19], modified AES-GCM [20], DESNew1, DESNew2 [21], and rapid image encryption schemes with block permutation and block diffusion [22]. Algorithms based on chaotic maps [23; 24; 25; 26] have also been explored. Numerous other encryption algorithms have been researched and developed, yet an inherent weakness of encryption algorithms is their inability to conceal the fact of data transmission. In other words, a third party can easily detect the presence of transmitted information, even if that information has been encrypted. To address this issue, a solution utilized is the application of information hiding techniques (steganographic methods). Consequently, research and attention towards methods that protect hidden information is increasing. One approach of steganography involves the development of algorithms to embed messages within images, videos, or audio. Many authors have utilized the Least Significant Bits (LSB) method as the basis for designing steganographic methods [27; 28; 29]. Authors in [30] proposed an image hiding method based on Least Significant Bits (LSB) substitution, Pixel Indicators, and the Zigzag Method. They divided the cover image into four subimages and further divided these subimages into three color planes (R, G, B). Different Pixel Indicators were then applied to each subimage using a zigzag approach. This method achieved a signal-to-noise ratio over noise PSNR > 50 dB. However, due to the use of one of the color channels (R, G, B) as an indicator for the existence of information in the other two channels, the capacity for hidden information per pixel in the cover image is reduced. [PERSON], [PERSON], and colleagues [31] employed the LZW compression algorithm to compress hidden information, then used the LSB bits of the RGB image to hide the compressed information. Although compressing the hidden information reduces the length of the embedded information, it increases the encoding and decoding time due to the added compression and decompression processes. Another method based on pixel value differencing (PVD) has also been researched for embedding confidential information into images. [PERSON] [32] developed an image-hiding method based on modulus function (MF) and PVD. This method utilized the difference between consecutive pixel pairs to embed confidential information. Additionally, [PERSON] and [PERSON] used pixel stacking to increase the capacity for embedding information in images [33]. Meanwhile, [PERSON] and [PERSON] [34] proposed a combination of PVD, MF, and particle swarm optimization. Alongside LSB and PVD, there are numerous other traditional methods being developed, such as histogram shifting [35; 36], multiple-bit planes [37], and palettes [38]. In addition to the aforementioned traditional methods, deep learning is emerging as an effective approach for information hiding in images. [PERSON] and colleagues [39] employed generative adversarial networks (GAN) to create cover images. A GAN generator with a U-Net architecture was proposed by [PERSON] et al. [40]. Beyond creating cover images, [PERSON] and colleagues demonstrated the effectiveness of deep learning in encoding and decoding (embedding and extracting) messages [41].