Hiding Images Within Images

We present a system to hide a full color image inside another of the
same size with minimal quality loss to either image. 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 examine how the result is achieved and apply numerous
transformations to analyze if image quality in the host and hidden
image can be maintained. These transformation range from simple image
manipulations to sophisticated machine learning-based adversaries.
Two extensions to the basic system are presented that mitigate the
possibility of discovering the content of the hidden image. With
these extensions, not only can the hidden information be kept secure,
but the system can be used to hide even more than a single image.
Applications for this technology include image authentication,
digital watermarks, finding exact regions of image manipulation, and
storing meta-information about image rendering and content.

• Machine Perception

• Machine Intelligence