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An adversarial example, which is an input instance with small, intentional feature perturbations to machine learning models, represents a concrete problem in Artificial intelligence safety. As an emerging defense method to defend against adversarial examples, generative adversarial networks-based defense methods have recently been studied. However, the performance of the state-of-the-art generative adversarial networks-based defense methods is limited because the target deep neural network models with generative adversarial networks-based defense methods are robust against adversarial examples but make a false decision for legitimate input data . To solve the accuracy degradation of the generative adversarial networks-based defense methods for legitimate input data , we propose a new generative adversarial networks-based defense method, which is called Adversarially Robust Generative …