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ACHIEVING OPTIMAL adversarial ACCURACY FOR adversarial DEEP LEARNING USING STACKELBERG GAMES

Acta Mathematica Scientia 2022年第6期42卷 2399-2418页

作者：高小山刘爽于立佳 Academy of Mathematics and Systems Science Chinese Academy of SciencesUniversity of Chinese Academy of SciencesBeijing 100190China

The purpose of adversarial deep learning is to train robust DNNs against adversarial attacks,and this is one of the major research focuses of deep learning.Game theory has been used to answer some of the basic questions about adversarial deep learning,such as those regarding the existence of a classifier with optimal robustness and the existence of optimal adversarial samples for a given class of classifiers.In most previous works,adversarial deep learning was formulated as a simultaneous game and the strategy spaces were assumed to be certain probability distributions in order for the Nash equilibrium to exist.However,this assumption is not applicable to practical situations.In this paper,we give answers to these basic questions for the practical case where the classifiers are DNNs with a given structure;we do that by formulating adversarial deep learning in the form of Stackelberg games.The existence of Stackelberg equilibria for these games is proven.Furthermore,it is shown that the equilibrium DNN has the largest adversarial accuracy among all DNNs with the same structure,when Carlini-Wagner s margin loss is used.The trade-off between robustness and accuracy in adversarial deep learning is also studied from a game theoretical perspective.

关键词： adversarial deep learning Stackelberg game optimal robust DNN universal adversarial attack adversarial accuracy trade-off result

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