# Generative Image Steganography via Encoding Pose Keypoints
###### Abstract  
Existing generative image steganography methods typically encode secret information into latent vectors, which are transformed into the entangled features of generated images. This approach faces two main challenges: (1) Transmission can degrade the quality of stego-images, causing bit errors in information extraction. (2) High embedding capacity often reduces the accuracy of information extraction. To overcome these limitations, this paper presents a novel generative image steganography via encoding pose keypoints. This method employs an LSTM-based sequence generation model to embed secret information into the generation process of pose keypoint sequences. Each generated sequence is drawn as a keypoint connectivity graph, which serves as input with an original image to a trained pose-guided person image generation model (DPTN-TA) to generate an image with the target pose. The sender uploads the generated images to a public channel to transmit the secret information. On the receiver's side, an improved YOLOv8 pose estimation model extracts the pose keypoints from the stego-images and decodes the embedded secret information using the sequence generation model. Extensive experiments on the DeepFashion dataset show that the proposed method significantly outperforms state-of-the-art methods in information extraction accuracy, achieving 99.94%. It also achieves an average hiding capacity of 178.4 bits per image. This method is robust against common image attacks, such as salt and pepper noise, median filtering, compression, and screenshots, with an average bit error rate of less than 0.87%. Additionally, the method is optimized for fast inference and lightweight deployment, enhancing its real-world applicability. information hiding; pose guided person image generation task; high robustness pose estimate; anti-steganalysis; improved YOLOv8-Pose +
Footnote †: journal: Applied Sciences  
17 October 202  
rectly by slightly modifying the spatial or frequency domain of the cover image. For example, the least significant bit (LSB) method adjusts the least significant bit of pixels to hide information [3]. However, this method inevitably causes image distortion, so researchers proposed adaptive steganography methods such as S-UNIWARD [4], HILL [5], HUGO [6], and WOW [7]. These methods manually design distortion functions to minimize the overall image distortion so that secret information can be more stealthily embedded in the image. With the development of deep learning, researchers began to explore using deep learning to optimize traditional steganography algorithms. For example, [PERSON] et al. 's ASDL-GAN model uses the generative adversarial network (GAN) to automatically learn the distortion function and embedding change probability to find the optimal embedding position [8]. Although these methods reduce image distortion, as long as the cover image is modified, it can still leave detectable traces. Strong steganalysis tools such as XuNet [9] and YeNet [10] can detect these steganography methods effectively. Researchers have proposed coverless image steganography (CIS) to overcome the limitations of traditional methods. Early CIS approaches established a mapping strategy between secret information and cover images' features, allowing the sender to select cover images with corresponding features from a preconstructed database according to the hash representation of secret data [11; 12]. These approaches avoid modifying the cover image during data embedding, which enhances their ability to resist detection by steganalysis. However, as the size of the hidden message increases, the required number of candidates to cover images rises exponentially, creating a significant bottleneck in the hiding capacity. To address this limitation, CIS has evolved into generative image steganography (GIS), where secret information directly drives the generation of stego-images. Recent advances in GIS focus on encoding secret information into specific input formats fed into image generation networks (IGNs) to generate stego-images.