  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.
