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Susceptibility to adversarial examples is one of the major concerns in convolutional neural networks (CNNs) applications. Training the model with adversarial examples, known as adversarial training, is a common countermeasure to tackle such attacks. In reality, however, defenders are uninformed about how adversarial examples are generated by the attacker. Therefore, it is pivotal to utilize more g
