Quantum-inspired analysis of neural network vulnerabilities:the role of conjugate variables in system attacks

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外文摘要：Neural networks demonstrate vulnerability to small,non-random perturbations,emerging as adversarial attacks.Such attacks,born from the gradient of the loss function relative to the input,are discerned as input conjugates,revealing a systemic fragility within the network structure.Intriguingly,a mathematical congruence manifests between this mechanism and the quantum physics'uncertainty principle,casting light on a hitherto unanticipated interdisciplinarity.This inherent susceptibility within neural network systems is generally intrinsic,highlighting not only the innate vulnerability of these networks,but also suggesting potential advancements in the interdisciplinary area for understanding these black-box networks.

外文关键词：

neural networkadversarial attackaccuracy-robustness trade-offuncertainty principlequantum physics

作者：

Jun-Jie Zhang、Deyu Meng

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作者单位：

Division of Computational physics and Intelligent modeling,Northwest Institute of Nuclear Technology,Xi'an 710024,China

School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security,Xi'an Jiaotong University,Xi'an 710049,China

Pazhou Lab,Guangzhou 510335,China

基金：

National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

项目编号：

2020YFA0713900121052271222600462272375

出版年：

2024

DOI：

10.1093/nsr/nwae141

国家科学评论(英文版)

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ISSN：

年,卷(期)：2024.11(9)