# Fostering the Robustness of White-Box Deep Neural Network Watermarks by Neuron Alignment

arXiv: https://arxiv.org/abs/2112.14108 · HTML: https://mlsanity.com/paper/2112.14108/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 28 Dec 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017)
- [Attention is All You Need in Speech Separation](https://mlsanity.com/paper/2010.13154/) (2020)
- [Adversarial Frontier Stitching for Remote Neural Network Watermarking](https://mlsanity.com/paper/1711.01894/) (2017)
- [SoK: How Robust is Image Classification Deep Neural Network Watermarking? (Extended Version)](https://mlsanity.com/paper/2108.04974/) (2021)
- [How to Prove Your Model Belongs to You: A Blind-Watermark based Framework to Protect Intellectual Property of DNN](https://mlsanity.com/paper/1903.01743/) (2019)
- [Deep Model Intellectual Property Protection via Deep Watermarking](https://mlsanity.com/paper/2103.04980/) (2021)
- [Image coding for machines: an end-to-end learned approach](https://mlsanity.com/paper/2108.09993/) (2021)
- [Semi-Supervised Singing Voice Separation with Noisy Self-Training](https://mlsanity.com/paper/2102.07961/) (2021)
- [Secure Watermark for Deep Neural Networks with Multi-task Learning](https://mlsanity.com/paper/2103.10021/) (2021)
- [Regulating Ownership Verification for Deep Neural Networks: Scenarios, Protocols, and Prospects](https://mlsanity.com/paper/2108.09065/) (2021)

## Built on by (most cited first, 5 of 5)

- [Performance-lossless Black-box Model Watermarking](https://mlsanity.com/paper/2312.06488/) (2023)
- [Knowledge-Free Black-Box Watermark and Ownership Proof for Image Classification Neural Networks](https://mlsanity.com/paper/2204.04522/) (2022)
- [On Function-Coupled Watermarks for Deep Neural Networks](https://mlsanity.com/paper/2302.10296/) (2023)
- [Find the Lady: Permutation and Re-Synchronization of Deep Neural Networks](https://mlsanity.com/paper/2312.14182/) (2023)
- [MeMark: Membrane-Space Watermarking for Spiking Neural Networks](https://mlsanity.com/paper/2608.25738/) (2026)

## Abstract (arXiv)

The wide application of deep learning techniques is boosting the regulation of deep learning models, especially deep neural networks (DNN), as commercial products. A necessary prerequisite for such regulations is identifying the owner of deep neural networks, which is usually done through the watermark. Current DNN watermarking schemes, particularly white-box ones, are uniformly fragile against a family of functionality equivalence attacks, especially the neuron permutation. This operation can effortlessly invalidate the ownership proof and escape copyright regulations. To enhance the robustness of white-box DNN watermarking schemes, this paper presents a procedure that aligns neurons into the same order as when the watermark is embedded, so the watermark can be correctly recognized. This neuron alignment process significantly facilitates the functionality of established deep neural network watermarking schemes.