Shafahi et al. propose universal adversarial training, meaning training on universal adversarial examples. In contrast to regular adversarial examples, universal ones represent perturbations that cause a network to mis-classify many test images. In contrast to regular adversarial training, where several additional iterations are required on each batch of images, universal adversarial training only needs one additional forward/backward pass on each batch. The obtained perturbations for each batch are accumulated in a universal adversarial examples. This makes adversarial training more efficient, however reduces robustness significantly.

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