  Generative steganography is the process of hiding secret messages in
generated images instead of cover images. Existing studies on generative
steganography use GAN or Flow models to obtain high hiding message capacity and
anti-detection ability over cover images. However, they create relatively
unrealistic stego images because of the inherent limitations of generative
models. We propose Diffusion-Stego, a generative steganography approach based
on diffusion models which outperform other generative models in image
generation. Diffusion-Stego projects secret messages into latent noise of
diffusion models and generates stego images with an iterative denoising
process. Since the naive hiding of secret messages into noise boosts visual
degradation and decreases extracted message accuracy, we introduce message
projection, which hides messages into noise space while addressing these
issues. We suggest three options for message projection to adjust the trade-off
between extracted message accuracy, anti-detection ability, and image quality.
Diffusion-Stego is a training-free approach, so we can apply it to pre-trained
diffusion models which generate high-quality images, or even large-scale
text-to-image models, such as Stable diffusion. Diffusion-Stego achieved a high
capacity of messages (3.0 bpp of binary messages with 98% accuracy, and 6.0 bpp
with 90% accuracy) as well as high quality (with a FID score of 2.77 for 1.0
bpp on the FFHQ 64$\times$64 dataset) that makes it challenging to distinguish
from real images in the PNG format.
