Achieving optimal adversarial accuracy for adversarial deep learning using Stackelberg games

DOI10.1007/S10473-022-0613-YOpenAlexW4293560553MaRDI QIDQ2080981

Lijia Yu, Shuang Liu, Xiao-Shan Gao

Publication date: 12 October 2022

Published in: Acta Mathematica Scientia. Series B. (English Edition) (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2207.08137

zbMATH Keywords

Stackelberg gameadversarial accuracyadversarial deep learningoptimal robust DNNtrade-off resultuniversal adversarial attack

Mathematics Subject Classification ID

Robustness and adaptive procedures (parametric inference) (62F35) Learning and adaptive systems in artificial intelligence (68T05) Applications of game theory (91A80)

Cites Work

• On the Stackelberg strategy in nonzero-sum games
• Multiagent Systems
• Adversarial Risk via Optimal Transport and Optimal Couplings
• A Further Generalization of the Kakutani Fixed Point Theorem, with Application to Nash Equilibrium Points
• Game Theory and Machine Learning for Cyber Security
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