Publishing/sharing pretrained deep neural network (DNN) models is a common practice in the community of computer vision. The increasing popularity of pretrained models has made it a serious concern: how to protect the intellectual properties of model owners and avert illegal usages by malicious attackers. This article aims at developing a framework for watermarking DNNs, with a particular focus on low-level image processing tasks that map images to images. Using image denoising and superresolution as case studies, we develop a black-box watermarking method for pretrained models, which exploits the overparameterization of the DNNs in image processing. In addition, an auxiliary module for visualizing the watermark information is proposed for further verification. Extensive experiments show that the proposed watermarking framework has no noticeable impact on model performance and enjoys the robustness against the often-seen attacks.

中文翻译：

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在图像处理中为深层神经网络加水印。

发布/共享预训练的深度神经网络（DNN）模型是计算机视觉社区的一种常见做法。预训练模型的日益普及使其成为一个严重的问题：如何保护模型所有者的知识产权并避免恶意攻击者的非法使用。本文旨在开发一种为DNN加水印的框架，特别关注将图像映射到图像的低级图像处理任务。使用图像去噪和超分辨率作为案例研究，我们为预训练模型开发了黑盒水印方法，该方法利用了DNN在图像处理中的过度参数化。另外，提出了用于可视化水印信息的辅助模块以用于进一步验证。