# Generative Image Steganography via Encoding Pose Keypoints
## 4 Evaluation and Comparisons
### Application to a Different Dataset  
To enhance our work's practical relevance, we explore our method's potential applications beyond the original dataset. Face images, commonly used on social media in profile pictures and posts, offer an ideal cover for covert communication. Therefore, we evaluated our approach using widely recognized face datasets, such as CelebFaces Attributes (CelebA) [38]. The PGPIG model utilized in this study imposes stringent requirements on the training dataset, significantly limiting its applicability. Specifically, the dataset must be divided into groups, each containing images of the same object displaying multiple poses with consistent scales and simple backgrounds to learn target pose generation effectively. This study employed the Pix2 Pix model as an alternative to address these limitations. Pix2 Pix [39], a CGAN-based image to image translation model, integrates conditional information within the generator and discriminator framework. Combined with the U-Net architecture, it enables the generation of images tailored to specific input conditions, making it partic  
Figure 11: Promotional Tweet Example with Stego-Images. ularly suitable for structured image transformations, such as converting sketches into photorealistic representations. To implement the proposed methodology on the CelebA dataset, we designed the following experimental framework: (1) facial landmark annotations, comprising 68 keypoints per image, were extracted using dlib's detection library; (2) the dataset was filtered to retain 120,000 high-quality facial images based on the completeness of annotations; (3) the keypoint annotations were converted into facial sketches, which, paired with their corresponding real facial images, were used to train the Pix2 Pix model for translating sketches into realistic images; and (4) the sequence generation model and pose estimation network was trained using configurations similar to previous experiments, with redundant details omitted. Figure 12 illustrates the generated result for a randomly generated face keypoint sketch. This section conducts experimental validation based on the previously mentioned metrics. Hiding Capacity: The facial dataset's 68 densely distributed keypoints result in a limited selection space for each keypoint compared to the original dataset. This restriction becomes more pronounced for later keypoints in the sequence, which are increasingly constrained by the real data distribution. However, under optimal settings that balance visual naturalness, the large number of keypoints still provides a reasonable hiding capacity, averaging 84 bits/image. Image Generation Quality: Under the optimal settings balancing visual naturalness, the image quality is reflected by an FID of 53.74, demonstrating that the hiding process preserves the perceptual integrity of facial images, maintaining their indistinguishability from the originals. Robustness: The robustness of the method is evaluated using the same parameters and metrics as in Figure 9, yielding the following _BER_ results: 0.12% for lossless, 0.87% for Gaussian noise, 2.2% for salt and pepper noise, 1.72% for mean filtering, 1.79% for median filtering, 2.48% for Gaussian filtering, and 1.14% for JPEG compression. Compared to the original dataset, robustness against noise and filtering is slightly lower. This is attributed to the relatively concentrated distribution of keypoints, which makes these attacks more effective in hindering the extraction of the pose estimation network. Security: This section assesses the method's security, emphasizing resistance to the unauthorized extraction and confidentiality of hidden information. The dense distribution of facial keypoints, combined with a probabilistic thresholding strategy, introduces randomness, making reverse engineering significantly more challenging. Additionally, minimal perceptual artifacts reduce the risk of detection through visual or statistical analysis.