Hiding Images in Plain Sight: Deep Steganography
Steganography is the practice of concealing a secret message within another, ordinary, message. Commonly, steganography is used to unobtrusively hide a small message within the noisy regions of a larger image. In this study, we attempt to place a full size color image within another image of the same size. Deep neural networks are simultaneously trained to create the hiding and revealing processes and are designed to specifically work as a pair. The system is trained on images drawn randomly from the ImageNet database, and works well on natural images from a wide variety of sources. Beyond demonstrating the successful application of deep learning to hiding images, we carefully examine how the result is achieved and explore extensions. Unlike many popular steganographic methods that encode the secret message within the least significant bits of the carrier image, our approach compresses and distributes the secret image's representation across all of the available bits.
The authors present a new steganography technique based on deep neural networks to simultaneously conduct hiding and revealing as a pair. The main idea is to combine two images of the same size together. The trained process aims to compress the information from the secret image into the least noticeable portions of the cover image and consists of three processes: a prep-Network for encoding features, the Hiding Network creates a container image, and a Reveal Network for decoding the transmitted container image. On the positive side, the proposed technique seems novel and clever, although it uses/modifies existing deep learning frameworks and therefore should be viewed as an application paper. The experiments are comprehensive and the results are convincing. 
The technique, however, resembles greatly the image decomposition problem, e.g., for separating mixtures of the intrinsic and reflection layers in previous literatures. I'd hope the authors to clarify how the problems are different and if the proposed technique can be used to resolve the layer separation problem. More important, I wonder if existing approaches on layer separations can be used to directly decode your encrypted results. 
I am also a bit concerned about the practicality of the proposed technique.  First, since the container image will be transmitted and potentially intercepted. It appears that one can tell directly that the container image contains hidden information (the image contains ringing type visual artifacts). if that is the case, the approach is likely to undermine the effort. Second, if the cover and secret images appear similar, the technique may fail to robustly separate them. So a more interesting question is how to pick a suitable cover image for a specific secret image. But such discussions seem largely missing. Third, the requirement the cover and the secrete images should have the same size seems to be a major limitation. One would have to resize the images to make them match, it may increase file sizes, etc. 
Overall, I think the proposed approach is interesting but I have concerns on the practicality and would like to see comparisons with state-of-the-art layer separation techniques.