Deep image steganography is a data hiding technology that conceal data in
digital images via deep neural networks. However, existing deep image
steganography methods only consider the visual similarity of container images
to host images, and neglect the statistical security (stealthiness) of
container images. Besides, they usually hides data limited to image type and
thus relax the constraint of lossless extraction. In this paper, we address the
above issues in a unified manner, and propose deep image steganography that can
embed data with arbitrary types into images for secure data hiding and lossless
data revealing. First, we formulate the data hiding as an image colorization
problem, in which the data is binarized and further mapped into the color
information for a gray-scale host image. Second, we design a conditional
invertible neural network which uses gray-scale image as prior to guide the
color generation and perform data hiding in a secure way. Finally, to achieve
lossless data revealing, we present a multi-stage training scheme to manage the
data loss due to rounding errors between hiding and revealing processes.
Extensive experiments demonstrate that the proposed method can perform secure
data hiding by generating realism color images and successfully resisting the
detection of steganalysis. Moreover, we can achieve 100% revealing accuracy in
different scenarios, indicating the practical utility of our steganography in
the real-world.

该研究提出了一种新的基于深度神经网络的图像隐写术，能够在图像中嵌入任意类型的数据并实现安全的数据隐藏和无损的数据提取，同时也能够抵御隐写分析的检测，并在不同情境下实现 100% 的无误差数据提取。