# Generative Image Steganography via Encoding Pose Keypoints
## 4 Evaluation and Comparisons
### Hiding Capacity  
In the method proposed in this paper, the sequence generation process involves constructing a candidate set for each sequence element and mapping the secret information to the index within this candidate set to achieve information embedding. Thus, the hidden capacity of this method is directly influenced by the size of the candidate set, namely the selection space. Specifically, the hiding capacity of this method can be quantified by Formula (5).  
\[Capacity=\sum_{i=1}^{18}\log_{2}\text{Size}(Set_{i}) \tag{5}\]  
Although the two-dimensional image plane has a broad selection space, considering the need to generate visually reasonable human postures, there is a tendency to retain the indexes with higher probability values when constructing the candidate set. This paper sets separate probability thresholds \(\tau_{i}\) for the keypoints of different parts, which determine the conservativeness of their respective selection spaces. The setting of the probability threshold considers two factors: (1) the output of the sequence generation model conforms to the real data distribution. Hence, as the number of sequence elements increases, the probability distribution of the model output gradually shifts from uniform dispersion to concentration, thereby gradually restricting its selection space; (2) the distribution of some keypoints is relatively dense and inflexible, while others are relatively dispersed and flexible, resulting in the contraction or expansion of their selection spaces. Figure 8 showsthe annotation sequence of the posture keypoints. Among them, keypoint 1 is obtained from a fixed candidate set, so no corresponding threshold is set; the preset threshold for keypoints 2 to 5 is \(10^{-6}\); the preset thresholds for keypoints 6 to 8 and 13, and 14 are \(2\times 10^{-5}\) and \(10^{-6}\), respectively; the preset thresholds for keypoints 9 to 12 and 15 to 18 are \(10^{-9}\) and \(2\times 10^{-8}\), respectively.  
In this paper, 5000 sequences were randomly generated with different threshold settings, and the average hiding capacity of the proposed method under each setting was calculated. The corresponding example graph of keypoint connectivity was also provided for reference, as shown in Table 1. Under the optimal parameter configuration, the average hidden capacity of the proposed method reached 178.4 bits/image. It can be intuitively observed that as the overall threshold increases, the generated human pose keypoint sequences are more consistent with the real data distribution. On the other hand, if the threshold decreases, the generated sequences will deviate from the real data distribution.  
Since the model clothing images generated by the proposed method are typically presented in groups, transferring multiple stego-images simultaneously is more reasonable than other methods. Thus, in this paper, the stego-carrier is set as an image group containing four images of the same model in different poses. Experimentally, the hiding capacity of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Graph** & **Threshold** & **Capacity (Bits/Image)** \\ \hline  & \(10^{4}\times\tau\) & 84.7 \\ \hline  & \(10^{2}\times\tau\) & 121.4 \\ \hline  & \(\tau\) & 178.4 \\ \hline  & \(10^{-3}\times\tau\) & 227.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Embedding capacity in different states.  
Figure 8: Marking sequence of pose keypoints.  
the proposed method and common steganographic methods was compared, as shown in Table 2. Our results indicate that this method is significantly superior in hiding capacity to some existing steganographic methods.