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Fully Exploiting Every Real Sample: SuperPixel Sample Gradient Model Stealing
June 28, 2024, 4:20 a.m. | Yunlong Zhao, Xiaoheng Deng, Yijing Liu, Xinjun Pei, Jiazhi Xia, Wei Chen
cs.CR updates on arXiv.org arxiv.org
Abstract: Model stealing (MS) involves querying and observing the output of a machine learning model to steal its capabilities. The quality of queried data is crucial, yet obtaining a large amount of real data for MS is often challenging. Recent works have reduced reliance on real data by using generative models. However, when high-dimensional query data is required, these methods are impractical due to the high costs of querying and the risk of model collapse. In …
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