# Secure Watermark for Deep Neural Networks with Multi-task Learning

arXiv: https://arxiv.org/abs/2103.10021 · HTML: https://mlsanity.com/paper/2103.10021/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2021.

## Built on by (most cited first, 4 of 4)

- [When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection](https://mlsanity.com/paper/2308.03573/) (2023)
- [Fostering the Robustness of White-Box Deep Neural Network Watermarks by Neuron Alignment](https://mlsanity.com/paper/2112.14108/) (2021)
- [Regulating Ownership Verification for Deep Neural Networks: Scenarios, Protocols, and Prospects](https://mlsanity.com/paper/2108.09065/) (2021)
- [PoLO: Proof-of-Learning and Proof-of-Ownership at Once with Chained Watermarking](https://mlsanity.com/paper/2505.12296/) (2025)

## Abstract (arXiv)

Deep neural networks are playing an important role in many real-life applications. After being trained with abundant data and computing resources, a deep neural network model providing service is endowed with economic value. An important prerequisite in commercializing and protecting deep neural networks is the reliable identification of their genuine author. To meet this goal, watermarking schemes that embed the author's identity information into the networks have been proposed. However, current schemes can hardly meet all the necessary requirements for securely proving the authorship and mostly focus on models for classification. To explicitly meet the formal definitions of the security requirements and increase the applicability of deep neural network watermarking schemes, we propose a new framework based on multi-task learning. By treating the watermark embedding as an extra task, most of the security requirements are explicitly formulated and met with well-designed regularizers, the rest is guaranteed by using components from cryptography. Moreover, a decentralized verification protocol is proposed to standardize the ownership verification. The experiment results show that the proposed scheme is flexible, secure, and robust, hence a promising candidate in deep learning model protection.