@inproceedings{
peng2024ldstega,
title={{LDS}tega: Practical and Robust Generative Image Steganography based on  Latent Diffusion Models},
author={Yinyin Peng and Yaofei Wang and Donghui Hu and Kejiang Chen and Xianjin Rong and Weiming Zhang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=kEqGgMgIlu}
}
Generative image steganography has gained significant attention due to its ability to hide secret data during image generation. However, existing generative image steganography methods still face challenges in terms of controllability, usability, and robustness, making it difficult to apply real-world scenarios. To ensure secure and reliable communication, we propose a practical and robust generative image steganography based on Latent Diffusion Models, called LDStega. LDStega takes controllable condition text as input and designs an encoding strategy in the reverse process of the Latent Diffusion Models to couple latent space generation with data hiding. The encoding strategy selects a sampling interval from a candidate pool of truncated Gaussian distributions guided by secret data to generate the stego latent space. Subsequently, the stego latent space is fed into the Decoder to generate the stego image.  The receiver extracts the secret data from the globally Gaussian distribution of the lossy-reconstructed latent space in the reverse process. Experimental results demonstrate that LDStega achieves high extraction accuracy while controllably generating image content and saving the stego image in the widely used PNG and JPEG formats. Additionally, LDStega outperforms state-of-the-art techniques in resisting common image attacks.
LDStega: Practical and Robust Generative Image Steganography based on  Latent Diffusion Models
Yinyin Peng Yaofei Wang Donghui Hu Kejiang Chen Xianjin Rong Weiming Zhang