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Neural network security properties classified by complexity

By PulseAugur Editorial · [1 sources] ·

A new paper formalizes eight security properties of neural networks as decision problems, classifying their computational complexity. The research demonstrates that properties like non-interference and monotonicity are co-NP-complete over ReLU networks, while backdoor detection is Sigma_2^P-complete. The study also reveals that quantifying over parameters, as in bit-flip attacks, makes verification exists-R-complete even for simple networks. AI

IMPACT Provides a theoretical framework for understanding and verifying the security of neural networks, potentially guiding future research in robust AI.

RANK_REASON Academic paper detailing theoretical properties and complexity of 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 →

Neural network security properties classified by complexity

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Academic paper detailing theoretical properties and complexity of 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) · Adrian Wurm ·

    Security Properties of Neural Networks as Decision Problems

    arXiv:2609.39768v1 Announce Type: cross Abstract: Certifying a deployed neural network raises decision problems that the verification literature has not classified: whether the model carries a backdoor planted in its training data, whether a fault in its stored parameters can dri…

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