# Generative Image Steganography via Encoding Pose Keypoints
## 3 The Proposed Method
### Improved YOLOv8n-Pose  
To realize the information extraction process in the proposed steganography method, this paper employs YOLOv8n-Pose as the fundamental framework of the detection algorithm to enhance the detection accuracy of human pose keypoints and conduct optimizations specific to steganography applications for this framework. The improved YOLOv8n-Pose's network structure mainly consists of a backbone, a neck, and a head, as shown in Figure 5. The Backbone extracts low-level features from the input image using deep convolutional networks to capture essential information. The Neck then receives these features and refines them further. By leveraging modules such as the Feature Pyramid Network (FPN), the Neck fuses information across multiple scales, allowing the model to effectively handle targets of different sizes and enhancing the accuracy and robustness of pose estimation. Finally, the Head processes these refined features into joint locations for precise target pose recognition. Figure 4: Structure of the DPTN-TA. It contains a self-reconstruction branch for auxiliary source-to-source task, and a transformation branch for main source-to-target task. These two branches share partial weights and are communicated by a pose transformer module. To enhance the robustness of the model against image attacks, a random noise module was incorporated into the YOLOv8n-Pose, encompassing Gaussian noise, salt and pepper noise, median filtering, mean filtering, and JPEG compression. This module is designed to simulate the potential quality loss that an image might encounter during transmission. Specifically, the mean of Gaussian noise was set to 0, and the standard deviation was 15; the noise ratio of salt and pepper noise was 0.005, and the salt and pepper ratio was 1:1; both median filtering and mean filtering were configured with two types of convolution kernels sized at 3 \(\times\) 3 and 5 \(\times\) 5; the quality parameters of the JPEG compression were set at two levels: 70 and 90. By randomly introducing different noises during the training phase, the model can better adapt to minor fluctuations or noises in the input data, thereby reducing its sensitivity to input variations and improving its adaptability to quality loss during image transmission. In covert communication, the timeliness of information is directly related to the accuracy of decision-making and the efficacy of actions. It requires the sender to securely embed the information into the cover without notable delay and simultaneously demands that the receiver be capable of rapid decoding. In this paper, by introducing the lightweight network structure of HGNetv2 of RT-DETR [21] to replace the backbone network of YOLOv8n, the inference speed is accelerated while reducing the model complexity, thereby enhancing the decoding speed of this method. As depicted in Figure 5, the replaced backbone network is mainly composed of the following core modules: HGStem, HGBlock, DWConv, and Rep_HGBlock. The HGStem module is primarily constituted by convolutional layers and is accountable for extracting target features at the input stage of the network. Its structure is depicted in Figure 6a. Such a design guarantees that the backbone network can capture abundant feature information with a relatively small number of parameters and computational efforts. The HGBlock module initially employs a lightweight convolutional block (Light Conv) for feature extraction, then utilizes a convolutional block (Conv) with a kernel size of 1 \(\times\) 1 to adjust the number of channels and finally conducts feature fusion with the residual connec  
Figure 5: Structure of the improved YOLOv8n-Pose. tion of the adjusted features. Its structure is presented in Figure 6b. This module adopts a hierarchical processing mechanism; namely, each HGBlock processes different levels of data, and each level concentrates on capturing features within a specific range, realizing the hierarchical processing of information.