Transferable Watermarking to Self-supervised Pre-trained Graph Encoders by Trigger Embeddings
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909418821517312 |
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| author | Zhao, Xiangyu Wu, Hanzhou Zhang, Xinpeng |
| author_facet | Zhao, Xiangyu Wu, Hanzhou Zhang, Xinpeng |
| contents | Recent years have witnessed the prosperous development of Graph Self-supervised Learning (GSSL), which enables to pre-train transferable foundation graph encoders. However, the easy-to-plug-in nature of such encoders makes them vulnerable to copyright infringement. To address this issue, we develop a novel watermarking framework to protect graph encoders in GSSL settings. The key idea is to force the encoder to map a set of specially crafted trigger instances into a unique compact cluster in the outputted embedding space during model pre-training. Consequently, when the encoder is stolen and concatenated with any downstream classifiers, the resulting model inherits the `backdoor' of the encoder and predicts the trigger instances to be in a single category with high probability regardless of the ground truth. Experimental results have shown that, the embedded watermark can be transferred to various downstream tasks in black-box settings, including node classification, link prediction and community detection, which forms a reliable watermark verification system for GSSL in reality. This approach also shows satisfactory performance in terms of model fidelity, reliability and robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13177 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transferable Watermarking to Self-supervised Pre-trained Graph Encoders by Trigger Embeddings Zhao, Xiangyu Wu, Hanzhou Zhang, Xinpeng Cryptography and Security Recent years have witnessed the prosperous development of Graph Self-supervised Learning (GSSL), which enables to pre-train transferable foundation graph encoders. However, the easy-to-plug-in nature of such encoders makes them vulnerable to copyright infringement. To address this issue, we develop a novel watermarking framework to protect graph encoders in GSSL settings. The key idea is to force the encoder to map a set of specially crafted trigger instances into a unique compact cluster in the outputted embedding space during model pre-training. Consequently, when the encoder is stolen and concatenated with any downstream classifiers, the resulting model inherits the `backdoor' of the encoder and predicts the trigger instances to be in a single category with high probability regardless of the ground truth. Experimental results have shown that, the embedded watermark can be transferred to various downstream tasks in black-box settings, including node classification, link prediction and community detection, which forms a reliable watermark verification system for GSSL in reality. This approach also shows satisfactory performance in terms of model fidelity, reliability and robustness. |
| title | Transferable Watermarking to Self-supervised Pre-trained Graph Encoders by Trigger Embeddings |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2406.13177 |