  In this paper, we propose a novel DNN watermarking method that utilizes a
learnable image transformation method with a secret key. The proposed method
embeds a watermark pattern in a model by using learnable transformed images and
allows us to remotely verify the ownership of the model. As a result, it is
piracy-resistant, so the original watermark cannot be overwritten by a pirated
watermark, and adding a new watermark decreases the model accuracy unlike most
of the existing DNN watermarking methods. In addition, it does not require a
special pre-defined training set or trigger set. We empirically evaluated the
proposed method on the CIFAR-10 dataset. The results show that it was resilient
against fine-tuning and pruning attacks while maintaining a high
watermark-detection accuracy.
