  Nowadays, there are plenty of works introducing convolutional neural networks
(CNNs) to the steganalysis and exceeding conventional steganalysis algorithms.
These works have shown the improving potential of deep learning in information
hiding domain. There are also several works based on deep learning to do image
steganography, but these works still have problems in capacity, invisibility
and security. In this paper, we propose a novel CNN architecture named as
\isgan to conceal a secret gray image into a color cover image on the sender
side and exactly extract the secret image out on the receiver side. There are
three contributions in our work: (i) we improve the invisibility by hiding the
secret image only in the Y channel of the cover image; (ii) We introduce the
generative adversarial networks to strengthen the security by minimizing the
divergence between the empirical probability distributions of stego images and
natural images. (iii) In order to associate with the human visual system
better, we construct a mixed loss function which is more appropriate for
steganography to generate more realistic stego images and reveal out more
better secret images. Experiment results show that ISGAN can achieve
start-of-art performances on LFW, Pascal VOC2012 and ImageNet datasets.
