An Embarrassingly Simple Approach for Intellectual Property Rights Protection on Recurrent Neural Networks
Not on the map. Why?
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
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 29 references that are in the map. First posted on arXiv 3 Oct 2022.
Builds on
13 of 29 references.
References · 13
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2018Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooringcited by 334
- 2017Embedding Watermarks into Deep Neural Networkscited by 227
- 2015A Simple Way to Initialize Recurrent Networks of Rectified Linear Unitscited by 130
- 2017Adversarial Frontier Stitching for Remote Neural Network Watermarkingcited by 110
- 2021Protecting Intellectual Property of Language Generation APIs with Lexical Watermarkcited by 78
- 2021Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attackcited by 45
- 2016Compression of Neural Machine Translation Models via Pruningcited by 43
- 2020Passport-aware Normalization for Deep Model Protectioncited by 42
- 2016Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Poolingcited by 39
- 2016Multiplicative LSTM for sequence modellingcited by 27
Built on by
Citing papers per year: 2026: 1.
1 paper.