Generative Image Steganography via Encoding Pose Keypoints
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 inf...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/58 |
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| author | Yi Cao Wentao Ge Chengsheng Yuan Quan Wang |
| author_facet | Yi Cao Wentao Ge Chengsheng Yuan Quan Wang |
| author_sort | Yi Cao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-38841e6a780f46869fae752d46898724 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-38841e6a780f46869fae752d468987242025-01-10T13:14:18ZengMDPI AGApplied Sciences2076-34172024-12-011515810.3390/app15010058Generative Image Steganography via Encoding Pose KeypointsYi Cao0Wentao Ge1Chengsheng Yuan2Quan Wang3School of Computer Science, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaSchool of Computer Science, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaSchool of Computer Science, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaWuxi City Internet of Vehicles Key Laboratory, Wuxi University, Wuxi 214105, ChinaExisting 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.https://www.mdpi.com/2076-3417/15/1/58information hidingpose guided person image generation taskhigh robustness pose estimateanti-steganalysisimproved YOLOv8-Pose |
| spellingShingle | Yi Cao Wentao Ge Chengsheng Yuan Quan Wang Generative Image Steganography via Encoding Pose Keypoints Applied Sciences information hiding pose guided person image generation task high robustness pose estimate anti-steganalysis improved YOLOv8-Pose |
| title | Generative Image Steganography via Encoding Pose Keypoints |
| title_full | Generative Image Steganography via Encoding Pose Keypoints |
| title_fullStr | Generative Image Steganography via Encoding Pose Keypoints |
| title_full_unstemmed | Generative Image Steganography via Encoding Pose Keypoints |
| title_short | Generative Image Steganography via Encoding Pose Keypoints |
| title_sort | generative image steganography via encoding pose keypoints |
| topic | information hiding pose guided person image generation task high robustness pose estimate anti-steganalysis improved YOLOv8-Pose |
| url | https://www.mdpi.com/2076-3417/15/1/58 |
| work_keys_str_mv | AT yicao generativeimagesteganographyviaencodingposekeypoints AT wentaoge generativeimagesteganographyviaencodingposekeypoints AT chengshengyuan generativeimagesteganographyviaencodingposekeypoints AT quanwang generativeimagesteganographyviaencodingposekeypoints |