# SteganoGAN: High Capacity Image Steganography with GANs

Authors: Zhang, Kevin Alex and Cuesta-Infante, Alfredo and Xu, Lei and Veeramachaneni, Kalyan

Abstract: Image steganography is a procedure for hiding messages inside pictures. While other techniques such as cryptography aim to prevent adversaries from reading the secret message, steganography aims to hide the presence of the message itself. In this paper, we propose a novel technique for hiding arbitrary binary data in images using generative adversarial networks which allow us to optimize the perceptual quality of the images produced by our model. We show that our approach achieves state-of-the-art payloads of 4.4 bits per pixel, evades detection by steganalysis tools, and is effective on images from multiple datasets. To enable fair comparisons, we have released an open source library that is available online at [this https URL](https://github.com/DAI-Lab/SteganoGAN).

### Citation (Chicago Manual of Style 17th edition)

Zhang, Kevin Alex, Alfredo Cuesta-Infante, Lei Xu, and Kalyan Veeramachaneni. 2019. “SteganoGAN: High Capacity Image Steganography with GANs.” CoRR abs/1901.03892. http://arxiv.org/abs/1901.03892.

### BibTeX

@article{DBLP:journals/corr/abs-1901-03892,
author = {Zhang, Kevin Alex and Cuesta{-}Infante, Alfredo and Xu, Lei and Veeramachaneni, Kalyan},
title = {SteganoGAN: High Capacity Image Steganography with GANs},
journal = {CoRR},
volume = {abs/1901.03892},
year = {2019},
url = {http://arxiv.org/abs/1901.03892},
archiveprefix = {arXiv},
eprint = {1901.03892},
timestamp = {Fri, 01 Feb 2019 13:39:59 +0100},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1901-03892},
bibsource = {dblp computer science bibliography, https://dblp.org}
}