  Engineering a top-notch deep learning model is an expensive procedure that
involves collecting data, hiring human resources with expertise in machine
learning, and providing high computational resources. For that reason, deep
learning models are considered as valuable Intellectual Properties (IPs) of the
model vendors. To ensure reliable commercialization of deep learning models, it
is crucial to develop techniques to protect model vendors against IP
infringements. One of such techniques that recently has shown great promise is
digital watermarking. However, current watermarking approaches can embed very
limited amount of information and are vulnerable against watermark removal
attacks. In this paper, we present GradSigns, a novel watermarking framework
for deep neural networks (DNNs). GradSigns embeds the owner's signature into
the gradient of the cross-entropy cost function with respect to inputs to the
model. Our approach has a negligible impact on the performance of the protected
model and it allows model vendors to remotely verify the watermark through
prediction APIs. We evaluate GradSigns on DNNs trained for different image
classification tasks using CIFAR-10, SVHN, and YTF datasets. Experimental
results show that GradSigns is robust against all known counter-watermark
attacks and can embed a large amount of information into DNNs.
