Papers with model extraction attacks
Extracted BERT Model Leaks More Information than You Think! (2022.emnlp-main)
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| Challenge: | Existing pre-trained language models are vulnerable to model extraction attacks . model extraction can cause severe privacy leakage even when victim models are facilitated with state-of-the-art defensive strategies. |
| Approach: | They propose to launch an attribute-inference attack against an extracted BERT model to prevent privacy leakage. |
| Outcome: | The proposed attack can cause severe privacy leakage even when victim models are facilitated with state-of-the-art defensive strategies. |
Your Semantic-Independent Watermark is Fragile: A Semantic Perturbation Attack against EaaS Watermark (2025.findings-emnlp)
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| Challenge: | Embedding-as-a-Service (EaaS) is a successful business pattern but faces significant challenges related to various forms of copyright infringement. |
| Approach: | They propose a semantic-independent watermarking scheme that exploits semantic perturbation tests to bypass verification. |
| Outcome: | The proposed watermarking schemes possess semantic-independent characteristics and exploit semantic perturbation tests to bypass verification. |
MeaeQ: Mount Model Extraction Attacks with Efficient Queries (2023.emnlp-main)
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| Challenge: | Recent studies focus on limited-query budget settings and adopt random sampling or active learning-based sampling strategies on publicly available, unannotated data sources. |
| Approach: | They propose a model extraction attack with efficient Queries that uses a zero-shot sequence inference classifier to filter task-relevant data from a public text corpus instead of a problem domain-specific dataset. |
| Outcome: | The proposed method achieves higher similarity to the victim model than baselines while requiring fewer queries. |