Do More With Less: Architecture-Agnostic and Data-Free Extraction Attack Against Tabular Model

Summary

TabExtractor is a model extraction approach for tabular models that works without the victim's training data or knowledge of its architecture. It uses a tabular generator to synthesize informative samples and a contrastive tabular transformer as the clone backbone. Across six victim architectures and four real-world datasets, the extracted clone reaches 92-100% of the victim's accuracy.

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