# Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection

Source: [Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection](<https://www.sota2.com/research/paper/68fa6eee1e209131cea42f60>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:09:40.360Z

Authors: Yiming Li, Yang Bai, Yong Jiang, Yong Yang, Shu-Tao Xia, Bo Li

Published: 2022-09-27T12:56:56.000Z

## Abstract

Deep neural networks (DNNs) have demonstrated their superiority in practice. Arguably, the rapid development of DNNs is largely benefited from high-quality (open-sourced) datasets, based on which researchers and developers can easily evaluate and improve their learning methods. Since the data collection is usually time-consuming or even expensive, how to protect their copyrights is of great significance and worth further exploration. In this paper, we revisit dataset ownership verification. We find that existing verification methods introduced new security risks in DNNs trained on the protected dataset, due to the targeted nature of poison-only backdoor watermarks. To alleviate this problem, in this work, we explore the untargeted backdoor watermarking scheme, where the abnormal model behaviors are not deterministic. Specifically, we introduce two dispersibilities and prove their correlation, based on which we design the untargeted backdoor watermark under both poisoned-label and clean-label settings. We also discuss how to use the proposed untargeted backdoor watermark for dataset ownership verification. Experiments on benchmark datasets verify the effectiveness of our methods and their resistance to existing backdoor defenses. Our codes are available at \\url{https://github.com/THUYimingLi/Untargeted\_Backdoor\_Watermark}.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Dataset Watermarking on CheXpert (test)](<https://www.sota2.com/research/sota/dataset-watermarking-on-chexpert-test.md>): Dataset Watermarking; CheXpert (test); BA: 84.28.

- [Dataset Tracking on CIFAR-10](<https://www.sota2.com/research/sota/dataset-tracking-on-cifar-10.md>): Dataset Tracking; CIFAR-10; Component Score A 1: 4.

- [Dataset Tracking on FairFace](<https://www.sota2.com/research/sota/dataset-tracking-on-fairface.md>): Dataset Tracking; FairFace; Count A 1 (out of 20): 0.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2210.00875>)

[PDF](<https://arxiv.org/pdf/2210.00875.pdf>)