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As adversarial attacks pose a serious threat to the security of AI system inpractice, such attacks have been extensively studied in the context of computervision applications. However, few attentions have been paid to the adversarialresearch on automatic path finding. In this paper, we show dominant adversarialexamples are effective when targeting A3C path finding, and design a CommonDominant Adversarial Examples Generation Method (CDG) to generate dominantadversarial examples against any given map. In addition, we propose GradientBand-based Adversarial Training, which trained with a single randomly choosedominant adversarial example without taking any modification, to realize the"1:N" attack immunity for generalized dominant adversarial examples. Extensiveexperimental results show that, the lowest generation precision for CDGalgorithm is 91.91%, and the lowest immune precision for Gradient Band-basedAdversarial Training is 93.89%, which can prove that our method can realize thegeneralized attack immunity of A3C path finding with a high confidence.

Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding