  Deep learning, especially deep neural networks (DNNs), has been widely and
successfully adopted in many critical applications for its high effectiveness
and efficiency. The rapid development of DNNs has benefited from the existence
of some high-quality datasets ($e.g.$, ImageNet), which allow researchers and
developers to easily verify the performance of their methods. Currently, almost
all existing released datasets require that they can only be adopted for
academic or educational purposes rather than commercial purposes without
permission. However, there is still no good way to ensure that. In this paper,
we formulate the protection of released datasets as verifying whether they are
adopted for training a (suspicious) third-party model, where defenders can only
query the model while having no information about its parameters and training
details. Based on this formulation, we propose to embed external patterns via
backdoor watermarking for the ownership verification to protect them. Our
method contains two main parts, including dataset watermarking and dataset
verification. Specifically, we exploit poison-only backdoor attacks ($e.g.$,
BadNets) for dataset watermarking and design a hypothesis-test-guided method
for dataset verification. We also provide some theoretical analyses of our
methods. Experiments on multiple benchmark datasets of different tasks are
conducted, which verify the effectiveness of our method. The code for
reproducing main experiments is available at
\url{https://github.com/THUYimingLi/DVBW}.
