Abstract
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.
Highlights
INFORMATION hiding is most commonly associated with well publicized nefarious endeavors, such as secretly planning and coordinating criminal activities through hidden messages in images posted on public sites [1], [2], [3]
The average color error, per RGB channel, for the host image was 2.4 and 3.4 (/256) on the hidden. This was measured on two test sets: those from the ImageNet challenge (’validation’ images) and the second composed of 200 cell-phone and DSLR photos
We created a system composed of three deep neural networks to hide images within images
Summary
INFORMATION hiding is most commonly associated with well publicized nefarious endeavors, such as secretly planning and coordinating criminal activities through hidden messages in images posted on public sites [1], [2], [3]. Beyond the multitude of misuses, hiding information can be used for practical positive applications as well. Hidden images used as watermarks embed authorship and copyright information without visually distorting the image [4]. We provide a way to handle the rapidly growing problem of fake and partially altered images on popular social media and news sites. By hiding invisible markers throughout an image, even subtle alterations can be detected by analyzing the reconstruction of the markers – all without compromising the visual integrity of the viewed image [5]
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