# FedReverse: Multiparty Reversible Deep Neural Network Watermarking

arXiv: https://arxiv.org/abs/2312.05738 · HTML: https://mlsanity.com/paper/2312.05738/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 10 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://mlsanity.com/paper/1710.01878/) (2017)
- [Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring](https://mlsanity.com/paper/1802.04633/) (2018)
- [High Accuracy and High Fidelity Extraction of Neural Networks](https://mlsanity.com/paper/1909.01838/) (2019)
- [Thieves on Sesame Street! Model Extraction of BERT-based APIs](https://mlsanity.com/paper/1910.12366/) (2019)
- [FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model](https://mlsanity.com/paper/2211.07160/) (2022)
- [Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring](https://mlsanity.com/paper/2211.07138/) (2022)
- [Multiple Watermarking Algorithm Based on Spread Transform Dither Modulation](https://mlsanity.com/paper/1601.04522/) (2016)
- [Solving the Capsulation Attack against Backdoor-based Deep Neural Network Watermarks by Reversing Triggers](https://mlsanity.com/paper/2208.14127/) (2022)
- [Reversible Quantization Index Modulation for Static Deep Neural Network Watermarking](https://mlsanity.com/paper/2305.17879/) (2023)

## Abstract (arXiv)

The proliferation of Deep Neural Networks (DNN) in commercial applications is expanding rapidly. Simultaneously, the increasing complexity and cost of training DNN models have intensified the urgency surrounding the protection of intellectual property associated with these trained models. In this regard, DNN watermarking has emerged as a crucial safeguarding technique. This paper presents FedReverse, a novel multiparty reversible watermarking approach for robust copyright protection while minimizing performance impact. Unlike existing methods, FedReverse enables collaborative watermark embedding from multiple parties after model training, ensuring individual copyright claims. In addition, FedReverse is reversible, enabling complete watermark removal with unanimous client consent. FedReverse demonstrates perfect covering, ensuring that observations of watermarked content do not reveal any information about the hidden watermark. Additionally, it showcases resistance against Known Original Attacks (KOA), making it highly challenging for attackers to forge watermarks or infer the key. This paper further evaluates FedReverse through comprehensive simulations involving Multi-layer Perceptron (MLP) and Convolutional Neural Networks (CNN) trained on the MNIST dataset. The simulations demonstrate FedReverse's robustness, reversibility, and minimal impact on model accuracy across varying embedding parameters and multiple client scenarios.