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New research questions polynomial-time complexity of DNN model extraction

By PulseAugur Editorial · [1 sources] ·

A new research paper challenges the assumption that extracting information from deep neural networks (DNNs) is always a polynomial-time process. While previous work suggested that hard-label extraction attacks, which only reveal the final classification, are efficient, this paper argues that the complexity increases with network depth. The authors propose a novel cross-layer extraction method to overcome these limitations. AI

IMPACT Challenges the efficiency assumptions for model extraction attacks, potentially impacting intellectual property protection for DNNs.

RANK_REASON Academic paper published on arXiv discussing theoretical limitations of model extraction attacks on deep neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research questions polynomial-time complexity of DNN model extraction

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Academic paper published on arXiv discussing theoretical limitations of model extraction attacks on deep neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Akira Ito, Takayuki Miura, Yosuke Todo ·

    Is the Hard-Label Cryptanalytic Model Extraction Really Polynomial?

    arXiv:2510.06692v3 Announce Type: replace Abstract: Deep Neural Networks (DNNs) have attracted significant attention, and their internal models are now considered valuable intellectual assets. Extracting such a model via oracle access to a DNN is conceptually similar to extractin…

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