The Geometry of Last-Layer Model Stealing

Abstract:This paper uses geometry to explain how a machine learning model can be stolen using an already existing well-known method. The author has shown the exact conditions required to perfectly copy the final layer of a transformer network. When looking deeper into the hidden layers the author has explained clear limits. The author has also demonstrated that a hidden network cannot be fully reverse engineered just by looking at the final results. The research clearly maps out what can and cannot be stolen from a model.

Machine Learning (cs.LG)

arXiv:2606.06854 [cs.LG]

(or arXiv:2606.06854v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.06854 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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