  In the past, steganography was to embed text in a carrier, the sender Alice
and the recipient Bob share the key, and the text is extracted by Bob through
the key. If more information is embedded, the image is easily distorted. In
contrast, if there is less embedded information, the image maintains good
visual integrity, but does not meet our requirements for steganographic
capacity. In this paper, we focus on tackling these challenges and limitations
to improve steganographic capacity. An image steganography method based on
Fully Convolutional Dense Network(FC-DenseNet) was proposed by us. The hidden
network and the extracted network are trained at the same time. The dataset of
the deep neural network is derived from various natural images of ImageNet. The
experimental results show that the stego-image after steganography and the
secret image extracted from stego-imge have a visually good effect, and the
stego-image has high capacity and high peak signal to noise ratio.
Image-to-image full size hiding is implemented.
