# Generative Image Steganography via Encoding Pose Keypoints
## 5 Conclusions  
This paper proposes a generative steganographic method based on pose keypoint encoding. First, the sender uses a sequence generation network to generate the sequences of pose keypoints based on the secret information and draws a keypoint map; then, the keypoint map is combined with any person image, and a pose-guided person image generation model is used to generate a person image with a specific pose, which is transmitted through a public channel; finally, the receiver obtains the image from the public channel, uses a pose estimation model to extract keypoints, and then uses a sequence generation model to recover the secret information. Experimental results show that compared with other generative steganographic methods, the proposed method fully conforms to human daily communication content and achieves significant effects in steganalysis resistance, information extraction rate, and robustness. However, the method has certain limitations that must be addressed in future work:  
Balancing Visual Plausibility and Hiding Capacity: The selection of the keypoint probability threshold plays a critical role in balancing hiding capacity and image quality. Lowering the threshold can increase hiding capacity but may lead to unnatural poses, reducing visual plausibility. Conversely, higher thresholds maintain a natural visual appearance but may limit the practicality of the method due to reduced hiding capacity. Limited Robustness to Severe Attacks: The method is robust against minor attacks like Gaussian noise and low-quality JPEG compression, which are common in practical scenarios. However, severe attacks such as heavy blurring or significant image cropping can disrupt the pose keypoint structure, leading to performance decline. This underscores the need for enhanced robustness mechanisms to ensure reliability under challenging conditions. Dataset Dependency: The limitations of this method stem from the requirements of the pose-guided person image generation (PGPIG) model it employs. This model imposes strict conditions on training datasets, necessitating image groups of objects in consistent scales and simple backgrounds, each containing images with multiple poses of the same object. These constraints hinder the method's application to datasets with varying object scales or complex backgrounds. To adapt the method for other datasets, two approaches can be considered: (1) dataset preprocessing: filter and construct image groups where objects exhibit consistent scales and varied poses; (2) using an alternative model: select images of objects with similar scales and train image to image generation models like Pix2 Pix. While preliminary tests on a face dataset have demonstrated promising generalizability, broader applications remain constrained by these dataset-specific requirements. Future work could focus on expanding the method's potential by integrating 3D keypoint modeling and 3D pose estimation. Embedding secret information into constructing a 3D model and utilizing a 3D pose estimation model for decoding could address current limitations, such as the constrained number of keypoints and their 2D choice space. This advancement would enable a richer embedding space, enhancing both hiding capacity and robustness while preserving the natural appearance of generated images. Moreover, 3D pose estimation ensures more accurate information extraction by capturing depth and spatial relationships, making it less sensitive to perspective distortions and occlusions than 2D approaches. This enhancement could significantly broaden the method's applicability to diverse datasets and complex real-world scenarios, further solidifying its practicality and effectiveness. Conceptualization, W.G. and Y.C. ; formal analysis, W.G. ; investigation, W.G., Y.C. and C.Y. ; writing--original draft preparation, W.G. and Y.C. ; writing--reviewing and editing, W.G., Y.C., C.Y. and Q.W. ; visualization, W.G. ; supervision, C.Y. and Q.W. All authors have read and agreed to the published version of the manuscript.