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。

采样生成隐写术

Generative Steganography by Sampling

Semantic image inpainting is a challenging task where large missing regions
have to be filled based on the available visual data. Existing methods which
extract information from only a single image generally produce unsatisfactory
results due to the lack of high level context. In this paper, we propose a
novel method for semantic image inpainting, which generates the missing content
by conditioning on the available data. Given a trained generative model, we
search for the closest encoding of the corrupted image in the latent image
manifold using our context and prior losses. This encoding is then passed
through the generative model to infer the missing content. In our method,
inference is possible irrespective of how the missing content is structured,
while the state-of-the-art learning based method requires specific information
about the holes in the training phase. Experiments on three datasets show that
our method successfully predicts information in large missing regions and
achieves pixel-level photorealism, significantly outperforming the
state-of-the-art methods.

本文提出了一种基于生成模型的新型语义图像修复方法，该方法可在缺失的内容相互独立的情况下进行推理，并取得超越其他方法的像素级真实感。