## 1 Introduction  
With the rapid development of digital technology, information security is particularly important in many critical scenarios, such as military communications, commercial confidentiality protection, and personal privacy protection. Computer security [1] protects access to data and operating systems through a variety of mechanisms; however, traditional techniques are no longer able to resist some of the current attacks. Therefore, more and more researchers are exploring new defense techniques such as information hiding techniques [2]. Information hiding techniques, as a product of the ancient art of steganography combined with modern cryptography, hinge on the core principle of utilizing the redundancy of multimedia information and the visual masking of specific information by the human eye. This allows for the ingenious concealment of information within a medium, thus providing robust technical support for encrypted communication and digital content protection. Image steganography [3; 4; 5], a significant branch of information-hiding techniques within the realm of images, seeks to discrerely transmit secret information by leveraging the visual characteristics of human perception. Research in image steganography can be approached from various angles. One aspect focuses on how to efficiently and securely embed secret information into cover images, while another emphasizes maintaining a high degree of concealment against sophisticated detection methods, such as machine learning models. Traditional image steganography algorithms have laid the groundwork for research in this area, cleverly integrating sensitive information into cover images through the use of meticulously designed keys and specific embedding techniques. Traditional image steganography algorithms can generally be classified into two main categories: transform domain algorithms and spatial domain algorithms. Transform domain algorithms include discrete wavelet transform domain (DWT) [6; 7; 8], discrete Fourier transform domain (DFT) [9; 10; 11], and discrete cosine transform domain (DCT) [12; 13; 14; 15], achieving efficient information embedding through their unique transformation properties. Spatial domain algorithms involve the least significant bit (LSB) [16; 17], pixel value differencing (PVD) [18], and wavelet-based optimal weighting (WOW) [19], directly manipulating pixel spaces to achieve covert embedding of information. However, in the face of increasingly sophisticated machine learning techniques, thanks to the powerful learning and generalization capabilities of machine learning, it is possible to discover details that are difficult to detect by the human visual system in traditional steganography algorithms, thus threatening the security of secret information. To overcome this limitation, researchers have actively explored new image steganography methods. For example, the adaptive steganography algorithms S-UNIWARD [20] and J-UNIWARD [21] proposed by [PERSON] et al. Although these two algorithms take into account the factors of counter-attacks, their steganography is still challenged in the face of machine learning detection. In addition to this, most of the existing image steganography techniques focus on steganographing textual information or grey-scale images as secret images, while relatively little research has been done on color images. In this context, the proposed VRIS (Visually Robust Image Steganography) model aims to embed color images as secret information into cover images. By introducing random noise, optimizing embedding strategies, and utilizing adversarial training techniques, it seeks to deceive both the human visual system and machine learning detectors. This research not only provides a new direction and ideas for the development of color image steganography but also offers a technological breakthrough in the field of information security.