  By and large, existing Intellectual Property (IP) protection on deep neural
networks typically i) focus on image classification task only, and ii) follow a
standard digital watermarking framework that was conventionally used to protect
the ownership of multimedia and video content. This paper demonstrates that the
current digital watermarking framework is insufficient to protect image
captioning tasks that are often regarded as one of the frontiers AI problems.
As a remedy, this paper studies and proposes two different embedding schemes in
the hidden memory state of a recurrent neural network to protect the image
captioning model. From empirical points, we prove that a forged key will yield
an unusable image captioning model, defeating the purpose of infringement. To
the best of our knowledge, this work is the first to propose ownership
protection on image captioning task. Also, extensive experiments show that the
proposed method does not compromise the original image captioning performance
on all common captioning metrics on Flickr30k and MS-COCO datasets, and at the
same time it is able to withstand both removal and ambiguity attacks. Code is
available at https://github.com/jianhanlim/ipr-imagecaptioning
