# An Embarrassingly Simple Approach for Intellectual Property Rights Protection on Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2210.00743 · HTML: https://mlsanity.com/paper/2210.00743/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring](https://mlsanity.com/paper/1802.04633/) (2018)
- [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017)
- [A Simple Way to Initialize Recurrent Networks of Rectified Linear Units](https://mlsanity.com/paper/1504.00941/) (2015)
- [Adversarial Frontier Stitching for Remote Neural Network Watermarking](https://mlsanity.com/paper/1711.01894/) (2017)
- [Protecting Intellectual Property of Language Generation APIs with Lexical Watermark](https://mlsanity.com/paper/2112.02701/) (2021)
- [Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack](https://mlsanity.com/paper/2102.04362/) (2021)
- [Compression of Neural Machine Translation Models via Pruning](https://mlsanity.com/paper/1606.09274/) (2016)
- [Passport-aware Normalization for Deep Model Protection](https://mlsanity.com/paper/2010.15824/) (2020)
- [Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling](https://mlsanity.com/paper/1611.06639/) (2016)
- [Multiplicative LSTM for sequence modelling](https://mlsanity.com/paper/1609.07959/) (2016)
- [Protect, Show, Attend and Tell: Empowering Image Captioning Models with Ownership Protection](https://mlsanity.com/paper/2008.11009/) (2020)

## Built on by (most cited first, 1 of 1)

- [MeMark: Membrane-Space Watermarking for Spiking Neural Networks](https://mlsanity.com/paper/2608.25738/) (2026)

## Abstract (arXiv)

Capitalise on deep learning models, offering Natural Language Processing (NLP) solutions as a part of the Machine Learning as a Service (MLaaS) has generated handsome revenues. At the same time, it is known that the creation of these lucrative deep models is non-trivial. Therefore, protecting these inventions intellectual property rights (IPR) from being abused, stolen and plagiarized is vital. This paper proposes a practical approach for the IPR protection on recurrent neural networks (RNN) without all the bells and whistles of existing IPR solutions. Particularly, we introduce the Gatekeeper concept that resembles the recurrent nature in RNN architecture to embed keys. Also, we design the model training scheme in a way such that the protected RNN model will retain its original performance iff a genuine key is presented. Extensive experiments showed that our protection scheme is robust and effective against ambiguity and removal attacks in both white-box and black-box protection schemes on different RNN variants. Code is available at https://github.com/zhiqin1998/RecurrentIPR