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

Protect, Show, Attend and Tell: Empowering Image Captioning Models with Ownership Protection