# Regulating Ownership Verification for Deep Neural Networks: Scenarios, Protocols, and Prospects

arXiv: https://arxiv.org/abs/2108.09065 · HTML: https://mlsanity.com/paper/2108.09065/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 20 Aug 2021.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks](https://mlsanity.com/paper/1805.12185/) (2018)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [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)
- [Deep Model Intellectual Property Protection via Deep Watermarking](https://mlsanity.com/paper/2103.04980/) (2021)
- [GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing](https://mlsanity.com/paper/1907.04433/) (2019)
- [Piracy Resistant Watermarks for Deep Neural Networks](https://mlsanity.com/paper/1910.01226/) (2019)
- [Performance Comparison of Contemporary DNN Watermarking Techniques](https://mlsanity.com/paper/1811.03713/) (2018)
- [Secure Watermark for Deep Neural Networks with Multi-task Learning](https://mlsanity.com/paper/2103.10021/) (2021)
- [Towards Practical Watermark for Deep Neural Networks in Federated Learning](https://mlsanity.com/paper/2105.03167/) (2021)

## Built on by (most cited first, 2 of 2)

- [Fostering the Robustness of White-Box Deep Neural Network Watermarks by Neuron Alignment](https://mlsanity.com/paper/2112.14108/) (2021)
- [Knowledge-Free Black-Box Watermark and Ownership Proof for Image Classification Neural Networks](https://mlsanity.com/paper/2204.04522/) (2022)

## Abstract (arXiv)

With the broad application of deep neural networks, the necessity of protecting them as intellectual properties has become evident. Numerous watermarking schemes have been proposed to identify the owner of a deep neural network and verify the ownership. However, most of them focused on the watermark embedding rather than the protocol for provable verification. To bridge the gap between those proposals and real-world demands, we study the deep learning model intellectual property protection in three scenarios: the ownership proof, the federated learning, and the intellectual property transfer. We present three protocols respectively. These protocols raise several new requirements for the bottom-level watermarking schemes.