## 2 Related Work  
Research on steganography has made significant progress in recent years [22], from the introduction of adversarial samples to the application of Generative Adversarial Networks (GANs), YJII-based Graph Neural Networks (GNNs), and feature-space hiding strategies, which have collectively advanced the development of steganography. The work of [PERSON], [PERSON], and others [23; 24] has revealed vulnerabilities in machine learning models and inspired novel ideas for the advancement of steganography. The Deepfool method, proposed by [PERSON] et al. [25], significantly advanced the development of adversarial sample generation techniques. This method can efficiently generate adversarial samples capable of deceiving deep neural networks (DNNs), thereby providing new adversarial strategies for steganography. Moreover, the C&W++ method, proposed by [PERSON] et al. [26], not only enhances the generation efficiency of adversarial samples but also assesses the robustness of DNNs, offering a more comprehensive tool for adversarial sample generation and evaluation in the context of steganography. Based on the concept of GAN, [PERSON] et al. [27] proposed a GAN-based steganographic image generation method, which uses the generative adversarial mechanism of GAN to generate realistic encrypted images by training a generator network which are visually indistinguishable from the original images, so as to achieve the purpose of improving the covertness of steganography. With the continuous research and application of steganography, in order to meet the needs of different application scenarios, [PERSON] et al. [28] subsequently proposed a multi-scale GAN method, which is capable of generating encrypted images with different scales, and this feature makes the encrypted image adaptable to different resolutions and sizes while maintaining high covertness, which greatly enriches the application scenarios of steganography. However, despite the significant progress of multi-scale GAN methods in encrypted image generation, how to further improve the covertness of encrypted images and their resistance to steganalyzers is still an urgent problem to be solved. [PERSON] and colleagues proposed ASDL-GAN [29] based on Generative Adversarial Networks (GANs), which enable adversarial training between generators and discriminators through unsupervised learning, generating realistic steganographic images. Compared to traditional prior knowledge-based methods, this approach significantly enhances the imperceptibility of secret images while maintaining the statistical properties of the images. However, its steganographic effect may still be exposed when facing more advanced steganalyzers. In order to further improve the concealment of encrypted images and their resistance to steganalyzers, [PERSON] and others introduced SGAN [30], which embeds secret information into adversarial examples using a steganalysis network, enhancing the realism of cover images while bolstering the resistance of embedded images against steganalysis detection. Nevertheless, the semantic shortcomings of SGAN may lead to noticeable embedded images. To address this issue, [PERSON] and collaborators designed a new model based on graph neural networks (GNNs) [31], associating the color channels of secret and cover images with a multi-level embedding strategy. This approach achieves high concealment and relatively low distortion, resulting in visually more natural embedded images. On this basis, [PERSON] and colleagues introduced ISGAN [32], which tackles color channel strategies by embedding secret information within the Y channel of cover images, thus avoiding color distortion issues. Yet, there remains room for improvement in image quality and resistance to steganalysis detection. In targeting the deception and security of learning-based detection, [PERSON] et al. [33] proposed a steganography method based on feature embedding.