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Adversarial training, in which a network is trained on both adversarial andclean examples, is one of the most trusted defense methods against adversarialattacks. However, there are three major practical difficulties in implementingand deploying this method - expensive in terms of extra memory and computationcosts; accuracy trade-off between clean and adversarial examples; and lack ofdiversity of adversarial perturbations. Classical adversarial training usesfixed, precomputed perturbations in adversarial examples (input space). Incontrast, we introduce dynamic adversarial perturbations into the parameterspace of the network, by adding perturbation biases to the fully connectedlayers of deep convolutional neural network. During training, using only cleanimages, the perturbation biases are updated in the Fast Gradient Sign Directionto automatically create and store adversarial perturbations by recycling thegradient information computed. The network learns and adjusts itselfautomatically to these learned adversarial perturbations. Thus, we can achieveadversarial training with negligible cost compared to requiring a training setof adversarial example images. In addition, if combined with classicaladversarial training, our perturbation biases can alleviate accuracy trade-offdifficulties, and diversify adversarial perturbations.

Adversarial Training: embedding adversarial perturbations into the parameter space of a neural network to build a robust system