# Generative Image Steganography via Encoding Pose Keypoints
## 2 Relate Work  
### Generative Image Steganography  
The core concept of GIS is to create a new image as a stego-carrier using a secret message. Since the image is generated without modifying the original, it effectively resists detection by existing steganalysis methods. Current methods can be broadly divided into latent space and semantic label mapping.  
Latent space mapping-based methods encode secret information into a latent vector, which is then inputted into IGNs to generate a stego-image. For example, [PERSON] et al. [13] encoded secret information into the low-dimensional noise vector of a deep convolutional GAN (DCGAN) to generate the stego-image. They employed a CNN model as an extractor to ensure that the extracted noise vector remained consistent with the original noise, thus enabling the recovery of the secret information. In contrast, [PERSON] et al. [14] used WGAN-GP to enhance image naturalness and produce more realistic stego-images. [PERSON] et al. [15] proposed a novel steganography utilizing an image-decoupled autoencoder; here, the sender maps secret information onto a latent vector, which is then converted into structural features combined with randomly sampled texture features to form the stego-image. The receiver recovers these structural features via an encoder and maps back from latent vectors to retrieve secret information using a decoder. While these methods provide substantial hiding capacity, their robustness suffers due to the complex entangled relationship between image data and latent vectors. When attacked, such as image compression or screenshots, this often results in degraded performance. To enhance robustness, studies [22; 23] have employed the latent noise vector and feature decoupling properties of the StyleGAN model to develop steganographic frameworks that mitigate the impact of image attacks on information extraction.  
Semantic label mapping-based methods encode secret information into specific labels, which guide IGNs to generate stego-images with features aligned to these labels. For instance, [PERSON] et al. [16] trained a conditional GAN (CGAN) on the MNIST dataset, where secret data were encoded into integer labels (0 to 9), generating handwritten images based on these labels. This approach exhibits lower complexity concerning category labels; thus, attackers may readily perceive the labels associated with the concealed information. [PERSON] et al. [17] proposed a steganographic model for generating anime character images. In this model, secret data are mapped to attribute labels through an LSTM model, which are then used as inputs to an auxiliary classifier GAN (ACGAN) to produce anime character images. [PERSON] et al. [18] introduced a steganographic method grounded in image selection and feature editing processes: first, partial secret data are embedded via selecting cover images from a database; thereafter, the StarGAN model is trained using a feature-labeled face dataset. The remaining secret data are then mapped onto feature labels before StarGAN is employed to edit cover image features and yield the final stego-image. These methods achieve high information extraction rates and robustness by linking semantic features with corresponding labels. However, their capacity to represent semantic features is limited, typically allowing the concealment of only a few to tens of bits per image.