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.

本文提出了一种基于扩散模型的生成隐写方法 ——Diffusion-Stego，该方法采用对潜在噪声进行信息投影的方式来生成隐写图片，并通过迭代去噪的过程来优化生成效果。实验结果表明，Diffusion-Stego 在密度、质量及隐蔽性等方面均取得了较优表现。

Diffusion-Stego: 基于消息投影的无需训练扩散生成隐写术

Diffusion-Stego: Training-free Diffusion Generative Steganography via  Message Projection

Generative steganography (GS) is a new data hiding manner, featuring direct
generation of stego media from secret data. Existing GS methods are generally
criticized for their poor performances. In this paper, we propose a novel flow
based GS approach -- Generative Steganographic Flow (GSF), which provides
direct generation of stego images without cover image. We take the stego image
generation and secret data recovery process as an invertible transformation,
and build a reversible bijective mapping between input secret data and
generated stego images. In the forward mapping, secret data is hidden in the
input latent of Glow model to generate stego images. By reversing the mapping,
hidden data can be extracted exactly from generated stego images. Furthermore,
we propose a novel latent optimization strategy to improve the fidelity of
stego images. Experimental results show our proposed GSF has far better
performances than SOTA works.

本文提出了一种基于流的生成隐写方法 (Generative Steganographic Flow, GSF)，该方法提供了直接生成隐写图像而不需要覆盖图像的方法。我们采用可逆的双射映射方法，在输入的秘密数据和生成的隐写图像之间建立一个可逆的双射映射。此外，我们提出了一种新的潜在优化策略来提高隐写图像的保真度。实验结果表明，GSF 比之前的方法有更好的性能。

生成式隐写流

Generative Steganographic Flow

Generative steganography (GS) is an emerging technique that generates stego
images directly from secret data. Various GS methods based on GANs or Flow have
been developed recently. However, existing GAN-based GS methods cannot
completely recover the hidden secret data due to the lack of network
invertibility, while Flow-based methods produce poor image quality due to the
stringent reversibility restriction in each module. To address this issue, we
propose a novel GS scheme called "Generative Steganography Diffusion" (GSD) by
devising an invertible diffusion model named "StegoDiffusion". It not only
generates realistic stego images but also allows for 100\% recovery of the
hidden secret data. The proposed StegoDiffusion model leverages a non-Markov
chain with a fast sampling technique to achieve efficient stego image
generation. By constructing an ordinary differential equation (ODE) based on
the transition probability of the generation process in StegoDiffusion, secret
data and stego images can be converted to each other through the approximate
solver of ODE -- Euler iteration formula, enabling the use of irreversible but
more expressive network structures to achieve model invertibility. Our proposed
GSD has the advantages of both reversibility and high performance,
significantly outperforming existing GS methods in all metrics.

本文提出了一种新的生成隐写术方案，即 Generative Steganography Diffusion (GSD)，通过设计一种可逆扩散模型 StegoDiffusion 来生成逼真的隐写图像，并能够 100％地恢复隐藏的秘密数据，该方案具有可逆性和高性能的优点，将现有方法在所有指标上显著优化。

生成隐写扩散

Generative Steganography Diffusion

In this paper, a novel data-driven information hiding scheme called
generative steganography by sampling (GSS) is proposed. Unlike in traditional
modification-based steganography, in our method the stego image is directly
sampled by a powerful generator: no explicit cover is used. Both parties share
a secret key used for message embedding and extraction. The Jensen-Shannon
divergence is introduced as a new criterion for evaluating the security of
generative steganography. Based on these principles, we propose a simple
practical generative steganography method that uses semantic image inpainting.
The message is written in advance to an uncorrupted region that needs to be
retained in the corrupted image. Then, the corrupted image with the secret
message is fed into a Generator trained by a generative adversarial network
(GAN) for semantic completion. Message loss and prior loss terms are proposed
for penalizing message extraction error and unrealistic stego image. In our
design, we first train a generator whose training target is the generation of
new data samples from the same distribution as that of existing training data.
Next, for the trained generator, backpropagation to the message and prior loss
are introduced to optimize the coding of the input noise data for the
generator. The presented experiments demonstrate the potential of the proposed
framework based on both qualitative and quantitative evaluations of the
generated stego images.

本文提出一种新的数据驱动信息隐藏方案，称为采样生成式隐写术（GSS），引入了 Jensen-Shannon 离散度作为评估隐写术安全性的新标准。通过基于生成对抗网络训练的生成器，实现语义图像修复进而嵌入信息。实验结果表明，该方案在定性和定量评估方面具有潜力。