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.

中文翻译：

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失真不可知深水印

水印是将信息嵌入到图像中的过程，该图像可以在失真下幸存下来，同时要求编码图像与原始图像几乎没有或没有感知差异。最近，基于深度学习的方法在各种图像失真下在视觉质量和消息有效载荷方面取得了令人瞩目的成果。然而，这些方法都需要在训练时图像失真的可微模型，并且可能很难泛化到未知的失真。这是不可取的，因为应用于带水印图像的失真类型通常是未知的和不可区分的。在本文中，我们提出了一种新的失真不可知水印框架，其中图像失真在训练期间没有明确建模。相反，我们系统的稳健性来自两个来源：对抗训练和信道编码。与在一组固定的失真和噪声水平上进行训练相比，我们的方法在训练期间可用的失真上取得了可比或更好的结果，并在未知失真上获得了更好的性能。