## 2 Related Work
### Image Steganography  
Traditional image steganography is broadly categorized into spatial and transform domains. Spatial techniques, like the LSB algorithm, are simple but often result in noticeable quality degradation and artifacts. Transform domain methods, involving DFT, DWT, and DCT, aim to embed data into spectral components but may overlook natural pixel distribution, impacting image quality. Researchers address this by exploring steganography in complex image texture regions, minimizing steganographic distortion to embed pixels judiciously. Approaches like S-UNIWARD [17], WOW [18], and HUGO [19] adopt this concept. However, these methods still rely on human-designed processes, making them vulnerable to steganalysis attacks. The integration of deep learning into image steganography has spurred the development of various methods, broadly categorized into three classes based on the embedding process: (1) Enhanced Cover Image Generation; (2) Steganographic Distortion Framework Design; and (3) Stego Image Generation. In the class of enhanced cover image generation, most methods utilize a Generative Adversarial Network (GAN) to create cover images with intricate textures suitable for steganography. [PERSON] et al. [9] were pioneers in using GANs for image steganography, employing the generator and discriminator for enhanced cover image generation. [PERSON] et al. [20] enhanced this method by incorporating the Wasserstein distance as the loss function and modifying the generative network structure, improving the quality of the generated images. However, this class faces challenges in reduced steganographic capacity, and the potential for suspicion due to unrealistic cover images is a concern for attackers. The steganographic distortion framework class is centred on employing neural networks to formulate more rational distortion functions. Through adversarial training, the neural network learns the embedded distortion probability of each pixel, thereby guiding the modification of the cover image. [PERSON] et al. [10] introduced a GAN to automatically learn steganographic distortion in images. This method proved effective in comparison with human-designed adaptive steganography algorithms and demonstrated notable resistance against steganalysis attacks. The category of stego image generation integrates encoder-decoder networks into the realm of image steganography, showcasing continued advancements. [PERSON] et al. [13] introduced the HayesGAN steganographic network, pioneering the concealment of text information within images using coding networks. This method involves inputting text information and cover images into the encoder network to obtain stego images, which are then reconstructed by the decoder network. To enhance security, the stego image and cover image undergo steganography analysis and detection through a discriminative network. [PERSON] et al. [21] significantly increased the capacity for hidden text information with the SteganoGAN, which, achieving a large-capacity image steganography of 4.4 bpp, this network produces more realistic stego images. [PERSON] [15], employing DCGAN, achieved steganography for colour image of the same size. The network structure comprises three components: a pre-processing network resizing the secret image to match the cover image's size, an encoder network combining the secret image and cover image to generate the stego image, and a decoder network facilitating the extraction of the secret image. [PERSON] et al. [22] made improvements on this foundation, but both steganographic networks led to the issue of colour distortion. [PERSON] et al. [23] addressed this by converting the RGB format of the steganography-performed image to YCrYb format. Since the Y channel exclusively retained semantic information without colour details, the secret grey image was hiding in the Y-channel of cover image. Subsequently, the obtained stego image was combined with the Cr and Cb channel images,effectively resolving the colour distortion problem.