  The rapid development of deep learning has benefited from the release of some
high-quality open-sourced datasets ($e.g.$, ImageNet), which allows researchers
to easily verify the effectiveness of their algorithms. Almost all existing
open-sourced datasets require that they can only be adopted for academic or
educational purposes rather than commercial purposes, whereas there is still no
good way to protect them. In this paper, we propose a \emph{backdoor embedding
based dataset watermarking} method to protect an open-sourced
image-classification dataset by verifying whether it is used for training a
third-party model. Specifically, the proposed method contains two main
processes, including \emph{dataset watermarking} and \emph{dataset
verification}. We adopt classical poisoning-based backdoor attacks ($e.g.$,
BadNets) for dataset watermarking, ie, generating some poisoned samples by
adding a certain trigger ($e.g.$, a local patch) onto some benign samples,
labeled with a pre-defined target class. Based on the proposed backdoor-based
watermarking, we use a hypothesis test guided method for dataset verification
based on the posterior probability generated by the suspicious third-party
model of the benign samples and their correspondingly watermarked samples
($i.e.$, images with trigger) on the target class. Experiments on some
benchmark datasets are conducted, which verify the effectiveness of the
proposed method.
