  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.

Cryptography and Security;Artificial Intelligence