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

arXiv: https://arxiv.org/abs/2008.11009 · HTML: https://mlsanity.com/paper/2008.11009/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 20 of 20)

- [Protecting Intellectual Property of Language Generation APIs with Lexical Watermark](https://mlsanity.com/paper/2112.02701/) (2021)
- [CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks](https://mlsanity.com/paper/2209.08773/) (2022)
- [Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark](https://mlsanity.com/paper/2305.10036/) (2023)
- [Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature Attribution](https://mlsanity.com/paper/2405.04825/) (2024)
- [FedIPR: Ownership Verification for Federated Deep Neural Network Models](https://mlsanity.com/paper/2109.13236/) (2021)
- [FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model](https://mlsanity.com/paper/2211.07160/) (2022)
- [Rethinking White-Box Watermarks on Deep Learning Models under Neural Structural Obfuscation](https://mlsanity.com/paper/2303.09732/) (2023)
- [Functional Invariants to Watermark Large Transformers](https://mlsanity.com/paper/2310.11446/) (2023)
- [Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond](https://mlsanity.com/paper/2306.00419/) (2023)
- [Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attacks](https://mlsanity.com/paper/2304.11285/) (2023)
- [WARDEN: Multi-Directional Backdoor Watermarks for Embedding-as-a-Service Copyright Protection](https://mlsanity.com/paper/2403.01472/) (2024)
- [Cracking White-box DNN Watermarks via Invariant Neuron Transforms](https://mlsanity.com/paper/2205.00199/) (2022)
- [DeepEclipse: How to Break White-Box DNN-Watermarking Schemes](https://mlsanity.com/paper/2403.03590/) (2024)
- [Robust and Minimally Invasive Watermarking for EaaS](https://mlsanity.com/paper/2410.17552/) (2024)
- [DEVICE: Depth and Visual Concepts Aware Transformer for OCR-based Image Captioning](https://mlsanity.com/paper/2302.01540/) (2023)
- [Performance-lossless Black-box Model Watermarking](https://mlsanity.com/paper/2312.06488/) (2023)
- [Proactive Schemes: A Survey of Adversarial Attacks for Social Good](https://mlsanity.com/paper/2409.16491/) (2024)
- [Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network Watermarking](https://mlsanity.com/paper/2507.11137/) (2025)
- [An Embarrassingly Simple Approach for Intellectual Property Rights Protection on Recurrent Neural Networks](https://mlsanity.com/paper/2210.00743/) (2022)
- [MeMark: Membrane-Space Watermarking for Spiking Neural Networks](https://mlsanity.com/paper/2608.25738/) (2026)

## Abstract (arXiv)

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