  Generative Adversarial Networks (GANs) have been widely used in various
application scenarios. Since the production of a commercial GAN requires
substantial computational and human resources, the copyright protection of GANs
is urgently needed. This paper presents a novel fingerprinting scheme for the
Intellectual Property (IP) protection of image-to-image GANs based on a trusted
third party. We break through the stealthiness and robustness bottlenecks
suffered by previous fingerprinting methods for classification models being
naively transferred to GANs. Specifically, we innovatively construct a
composite deep learning model from the target GAN and a classifier. Then we
generate fingerprint samples from this composite model, and embed them in the
classifier for effective ownership verification. This scheme inspires some
concrete methodologies to practically protect the modern image-to-image
translation GANs. Theoretical analysis proves that these methods can satisfy
different security requirements necessary for IP protection. We also conduct
extensive experiments to show that our solutions outperform existing
strategies.
