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.
@article{arxiv.1806.06357,
title = {StegNet: Mega Image Steganography Capacity with Deep Convolutional Network},
author = {Pin Wu and Yang Yang and Xiaoqiang Li},
journal= {arXiv preprint arXiv:1806.06357},
year = {2018}
}