  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.
