Ever since Machine Learning as a Service (MLaaS) emerges as a viable business
that utilizes deep learning models to generate lucrative revenue, Intellectual
Property Right (IPR) has become a major concern because these deep learning
models can easily be replicated, shared, and re-distributed by any unauthorized
third parties. To the best of our knowledge, one of the prominent deep learning
models - Generative Adversarial Networks (GANs) which has been widely used to
create photorealistic image are totally unprotected despite the existence of
pioneering IPR protection methodology for Convolutional Neural Networks (CNNs).
This paper therefore presents a complete protection framework in both black-box
and white-box settings to enforce IPR protection on GANs. Empirically, we show
that the proposed method does not compromise the original GANs performance
(i.e. image generation, image super-resolution, style transfer), and at the
same time, it is able to withstand both removal and ambiguity attacks against
embedded watermarks.

本文提出了一个黑盒和白盒设置的完整保护框架，对 GAN 进行知识产权保护，以抵御恶意的复制、分享和重新分发。实证分析表明，该方法不会影响原始 GAN 的性能，同时能够抵御嵌入式水印的删除和模糊攻击。