  Image hiding is the study of techniques for covert storage and transmission,
which embeds a secret image into a container image and generates stego image to
make it similar in appearance to a normal image. However, existing image hiding
methods have a serious problem that the hiding and revealing process cannot be
fully invertible, which results in the revealing network not being able to
recover the secret image losslessly, which makes it impossible to
simultaneously achieve high fidelity and secure transmission of the secret
image in an insecure network environment. To solve this problem,this paper
proposes a fully invertible image hiding architecture based on invertible
neural network,aiming to realize invertible hiding of secret images,which is
invertible on both data and network. Based on this ingenious architecture, the
method can withstand deep learning based image steganalysis. In addition, we
propose a new method for enhancing the robustness of stego images after
interference during transmission. Experiments demonstrate that the FIIH
proposed in this paper significantly outperforms other state-of-the-art image
hiding methods in hiding a single image, and also significantly outperforms
other state-of-the-art methods in robustness and security.
