Mining adversarial patterns via regularized loss minimization

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On the (Statistical) Detection of Adversarial Examples (2017, methodology): “Other approaches make use of game theory [6, 9, 21].”
A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Examples (2016, background): “…of membership queries (Lowd & Meek, 2005), formalizing the attack process as a two-person sequential Stackelberg game (Brückner & Scheffer, 2011; Liu & Chawla, 2010), a min-max strategy (training a classiﬁer with best performance under the worst perturbation) (Dekel et al., 2010; Globerson &…”
Optimal randomized classification in adversarial settings (2014, background): “The study of machine learning in such settings has come to be known as adversarial machine learning [8, 16, 3, 5, 15, 2, 6, 14, 10, 7, 18].”
Pattern Recognition Systems under Attack: Design Issues and Research Challenges (2014, background): “[31]; then, more rigorous approaches have introduced Nash and Stackelberg games for secure learning, deriving formal conditions for the corresponding equilibrium strategies [25, 45, 49, 26, 24].”