# Generative Image Steganography via Encoding Pose Keypoints
###### Abstract
The receiver can then use an extractor to recover the embedded information from these stego-images. Some studies have observed that natural images follow complex, structured distributions, and neural networks are effective at learning the relationships between image spatial distributions and latent space distributions, such as high-dimensional Gaussian distributions. Building on these observations, researchers have developed methods to encode secret information into latent vectors, which are subsequently transformed into the corresponding stego-images using IGNs [13; 14; 15]. While these methods achieve high hiding capacities, their robustness is often compromised due to the entangled relationships between image data and latent vectors. This leads to significant performance degradation under attacks like image compression or screenshots. To improve robustness, some studies have proposed mapping secret information to specific labels or feature codes. This enables the generation of images whose attributes align with the distribution of these labels, ensuring that the generated stego-images retain their normal semantic meaning while effectively concealing the embedded information [16; 17; 18]. Although these methods offer high information extraction accuracy and robustness through semantic feature-label associations, their ability to express complex semantic features is still limited. Typically, they can only embed a small number of bits--ranging from a few to tens of bits--of secret information per image. In summary, although current GIS methods effectively resist steganalysis, they often struggle to balance hiding capacity, information extraction accuracy, and robustness, and they are difficult to apply in practical deployment. To address these limitations, this paper introduces a generative image steganography via encoding pose keypoints. Specifically, this approach (1) embeds secret data into low-level semantic features, such as pose keypoints, which are highly separable and stable, ensuring high information extraction accuracy even under various image attacks; and (2) exploits the finer-grained feature representation of low-level semantics (e.g., outline, edge, texture) compared to high-level semantics (e.g., facial features, object labels), thus enhancing the hiding capacity. The key contributions of this paper are summarized as follows:  
1. A sequence generation model is designed based on the Long Short-Term Memory (LSTM) network [19]. Using a maximum likelihood estimation strategy, the network is trained on a specific keypoint annotation dataset to output probability distributions consistent with the real dataset, thus generating natural human poses. A mapping strategy is proposed to reduce the complexity of the sequence generation model in a dimension-reducing manner and improve computational efficiency. 2. To meet the needs of practical steganography applications, the pose estimation network YOLOv8-Pose [20] is improved by adding a noise module to enhance its ability to adapt to quality loss during image transmission. At the same time, the lightweight network structure of HGNetv2 from RT-DETR [21] is introduced to replace the backbone network of YOLOv8-Pose, reducing the model complexity and improving the inference speed, thereby effectively improving the decoding efficiency. In addition, several modules in HGNetv2 are improved. The structure of this paper is as follows: Section 2 reviews related work in GIS, pose-guided person image generation (PGPIG), and human pose estimation (HPE). Section 3 details the proposed method involving the sequence generation model, the PGPIG model, and the improved YOLOv8-Pose network. Section 4 presents the evaluation and comparisons, and Section 5 concludes the paper and outlines directions for future work.