  Watermarking is the process of embedding information into an image that can
survive under distortions, while requiring the encoded image to have little or
no perceptual difference from the original image. Recently, deep learning-based
methods achieved impressive results in both visual quality and message payload
under a wide variety of image distortions. However, these methods all require
differentiable models for the image distortions at training time, and may
generalize poorly to unknown distortions. This is undesirable since the types
of distortions applied to watermarked images are usually unknown and
non-differentiable. In this paper, we propose a new framework for
distortion-agnostic watermarking, where the image distortion is not explicitly
modeled during training. Instead, the robustness of our system comes from two
sources: adversarial training and channel coding. Compared to training on a
fixed set of distortions and noise levels, our method achieves comparable or
better results on distortions available during training, and better performance
on unknown distortions.
