  The commercialization of deep learning creates a compelling need for
intellectual property (IP) protection. Deep neural network (DNN) watermarking
has been proposed as a promising tool to help model owners prove ownership and
fight piracy. A popular approach of watermarking is to train a DNN to recognize
images with certain \textit{trigger} patterns. In this paper, we propose a
novel evolutionary algorithm-based method to generate and optimize trigger
patterns. Our method brings a siginificant reduction in false positive rates,
leading to compelling proof of ownership. At the same time, it maintains the
robustness of the watermark against attacks. We compare our method with the
prior art and demonstrate its effectiveness on popular models and datasets.
