MemCatalyst: Amplifying Data Auditing on Vision-Language Models via Data Poisoning
Vision-Language models (VLMs) achieve outstanding performance largely due to the amount of training data available on the internet. At the same time, data holders (e.g., artists) urgently need to determine whether their data has been used for model training without authorization, which concerns both intellectual property rights and personal privacy. Data auditing, particularly through membership inference (MI), has attracted attention as a direct tool. This work proposes MemCatalyst, a set of data poisoning tools, aiming to amplify the data auditing performance on VLMs. MemCatalyst employs two strategies: Poisoning Text (PT) and Poisoning Image (PI). MemCatalyst forces VLMs to over-learn ...
| Authors | Xukun Luan, Jinyan Liu, Yuhui Gong et al. |
|---|---|
| Primary category | cs.CR |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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