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Poster
Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection
Yiming Li · Yang Bai · Yong Jiang · Yong Yang · Shu-Tao Xia · Bo Li

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #213
in Poster Session 6 »

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.

Keywords: Backdoor Attack · AI security · Copyright Protection · Dataset Protection · Ownership Verification
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Yiming Li (Tsinghua University)

Dr. Yiming Li is currently a Research Professor in the School of Cyber Science and Technology at Zhejiang University. Before that, he received his Ph.D. degree with honors in Computer Science and Technology from Tsinghua University (2023) and his B.S. degree with honors in Mathematics and Applied Mathematics from Ningbo University (2018). His research interests are in the domain of Trustworthy ML and AI Security, especially backdoor learning and copyright protection in deep learning. His research has been published in multiple top-tier conferences and journals, such as ICLR, NeurIPS, and IEEE TIFS. He served as the senior program committee member of AAAI, the program committee member of ICLR, NeurIPS, ICML, etc., and the reviewer of IEEE TPAMI, IEEE TIFS, IEEE TDSC, etc. His research has been featured by major media outlets, such as IEEE Spectrum. He was the recipient of the Best Paper Award in PAKDD 2023 and the Raising Star Award at WAIC 2023.

Yang Bai (Tencent Security Zhuque Lab)
Yong Jiang (Tsinghua)
Yong Yang
Shu-Tao Xia (Tsinghua University)
Bo Li (UIUC)

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