  Traditional image steganography often leans interests towards safely
embedding hidden information into cover images with payload capacity almost
neglected. This paper combines recent deep convolutional neural network methods
with image-into-image steganography. It successfully hides the same size images
with a decoding rate of 98.2% or bpp (bits per pixel) of 23.57 by changing only
0.76% of the cover image on average. Our method directly learns end-to-end
mappings between the cover image and the embedded image and between the hidden
image and the decoded image. We~further show that our embedded image, while
with mega payload capacity, is still robust to statistical analysis.
