# Towards Traitor Tracing in Black-and-White-Box DNN Watermarking with Tardos-based Codes

arXiv: https://arxiv.org/abs/2307.06695 · HTML: https://mlsanity.com/paper/2307.06695/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2023.

## Built on by (most cited first, 3 of 3)

- [When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection](https://mlsanity.com/paper/2308.03573/) (2023)
- [Performance-lossless Black-box Model Watermarking](https://mlsanity.com/paper/2312.06488/) (2023)
- [BlackCATT: Black-box Collusion Aware Traitor Tracing in Federated Learning](https://mlsanity.com/paper/2602.12138/) (2026)

## Abstract (arXiv)

The growing popularity of Deep Neural Networks, which often require computationally expensive training and access to a vast amount of data, calls for accurate authorship verification methods to deter unlawful dissemination of the models and identify the source of the leak. In DNN watermarking the owner may have access to the full network (white-box) or only be able to extract information from its output to queries (black-box), but a watermarked model may include both approaches in order to gather sufficient evidence to then gain access to the network. Although there has been limited research in white-box watermarking that considers traitor tracing, this problem is yet to be explored in the black-box scenario. In this paper, we propose a black-and-white-box watermarking method for DNN classifiers that opens the door to collusion-resistant traitor tracing in black-box, exploiting the properties of Tardos codes, and making it possible to identify the source of the leak before access to the model is granted. While experimental results show that the method can successfully identify traitors, even when further attacks have been performed, we also discuss its limitations and open problems for traitor tracing in black-box.