  Secret information sharing through image carrier has aroused much research
attention in recent years with images' growing domination on the Internet and
mobile applications. However, with the booming trend of convolutional neural
networks, image steganography is facing a more significant challenge from
neural-network-automated tasks. To improve the security of image steganography
and minimize task result distortion, models must maintain the feature maps
generated by task-specific networks being irrelative to any hidden information
embedded in the carrier. This paper introduces a binary attention mechanism
into image steganography to help alleviate the security issue, and in the
meanwhile, increase embedding payload capacity. The experimental results show
that our method has the advantage of high payload capacity with little feature
map distortion and still resist detection by state-of-the-art image
steganalysis algorithms.
