Protecting Intellectual Property of Deep Neural Networks with Watermarking
Zhang, Jialong
and
Gu, Zhongshu
and
Jang, Jiyong
and
Wu, Hui
and
Stoecklin, Marc Ph.
and
Huang, Heqing
and
Molloy, Ian
ACM AsiaCCS - 2018 via Local Bibsonomy
Keywords:
dblp
Zhang et al. propose a watermarking approach to protect the intellectual property of deep neural network models. Here, the watermarking concept is generalized from multimedia; specifically, the purpose of a watermark is to uniquely identify a neural network model as the original owner’s property to avoid plagiarism. The problem is illustrated in Figure 1. As watermarks, the authors consider perturbed input images. During training, these perturbations are trained to produce very specific outputs, as illustrated in Figure 2. For example, random pixels are added, or text is added to images. After training, the model can be uniquely identified by these perturbed watermark images that are unrelated to the actual task.
https://i.imgur.com/TydqBwo.png
Figure 1: Illustration of the problem setting for watermarking.
https://i.imgur.com/5Zlei0z.png
Figure 2: Example watermarks.
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
